U.S. patent number 10,592,959 [Application Number 15/487,728] was granted by the patent office on 2020-03-17 for systems and methods for facilitating shopping in a physical retail facility.
This patent grant is currently assigned to Walmart Apollo, LLC. The grantee listed for this patent is Walmart Apollo, LLC. Invention is credited to Matthew A. Jones, Todd D. Mattingly, Robert J. Taylor, Jason R. Todd, Aaron J. Vasgaard, Tim W. Webb, Bruce W. Wilkinson.
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United States Patent |
10,592,959 |
Wilkinson , et al. |
March 17, 2020 |
Systems and methods for facilitating shopping in a physical retail
facility
Abstract
In some embodiments, apparatuses and methods are provided herein
useful to facilitate expedient shopping in a physical retail
facility. In one embodiment, a shopping system directed to
pre-filling shopping carts with retail items prior to a customer's
arrival at the physical retail shopping facility includes a user
database of user profiles having one or more partialities
associated with customers, a product database of retail products
with identified vectorized characterizations or product vectors, a
plurality of physical shopping carts, and a control circuit. By one
approach, the control circuit is configured to access the user
database and the product database and identify suggested retail
items for a particular customer based, in part, on comparisons
between the identified partialities and the identified vectorized
product characterizations of the retail products.
Inventors: |
Wilkinson; Bruce W. (Rogers,
AR), Jones; Matthew A. (Bentonville, AR), Vasgaard; Aaron
J. (Rogers, AR), Taylor; Robert J. (Rogers, AR),
Webb; Tim W. (Rogers, AR), Mattingly; Todd D.
(Bentonville, AR), Todd; Jason R. (Lowell, AR) |
Applicant: |
Name |
City |
State |
Country |
Type |
Walmart Apollo, LLC |
Bentonville |
AR |
US |
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Assignee: |
Walmart Apollo, LLC
(Bentonville, AR)
|
Family
ID: |
60040097 |
Appl.
No.: |
15/487,728 |
Filed: |
April 14, 2017 |
Prior Publication Data
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Document
Identifier |
Publication Date |
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US 20170300999 A1 |
Oct 19, 2017 |
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Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
Issue Date |
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62323026 |
Apr 15, 2016 |
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62348444 |
Jun 10, 2016 |
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62436842 |
Dec 20, 2016 |
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62485045 |
Apr 13, 2017 |
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62356375 |
Jun 29, 2016 |
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62356374 |
Jun 29, 2016 |
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62413487 |
Oct 27, 2016 |
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62402068 |
Sep 30, 2016 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06Q
30/0631 (20130101); G06Q 90/00 (20130101); G06F
16/288 (20190101) |
Current International
Class: |
G06Q
30/06 (20120101); G06Q 90/00 (20060101); G06F
16/28 (20190101) |
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|
Primary Examiner: Zeender; Florian M
Assistant Examiner: Poffenbarger; Whitney
Attorney, Agent or Firm: Fitch, Even, Tabin & Flannery
LLP
Parent Case Text
CROSS-REFERENCE TO RELATED APPLICATION
This application claims the benefit of each of the following U.S.
Provisional applications, each of which is incorporated herein by
reference in its entirety: 62/323,026 filed Apr. 15, 2016;
62/348,444 filed Jun. 10, 2016; 62/436,842 filed Dec. 20, 2016;
62/485,045, filed Apr. 13, 2017; 62/356,375, filed Jun. 29, 2016;
62/356,374, filed Jun. 29, 2016; 62/402,068, filed Sep. 30, 2016;
and 62/413,487, filed Oct. 27, 2016.
Claims
What is claimed is:
1. A system comprising: a database of user profiles, the user
profiles having one or more partialities associated therewith and
the user profiles being associated with particular customers; a
database of retail products, at least some of the retail products
having identified vectorized product characterizations; a control
circuit configured to access the database of user profiles and the
database of retail products and configured to identify one or more
suggested retail items for a particular customer based, in part, on
comparisons between the identified partialities of the user profile
associated with the particular customer and the identified
vectorized product characterizations of the retail products; a
plurality of physical shopping carts at a retail shopping facility,
one of the plurality of shopping carts being designated for the
particular customer and filled with at least one of the one or more
suggested retail items prior to the particular customer arriving at
the retail shopping facility; a rejection bin where the particular
customer may deposit unwanted suggested retail items, the rejection
bin having a bin sensor configured to detect placement of the
unwanted suggested retail items; and a product support system and a
product routing system communicatively coupled to the control
circuit, the product support system configured to present products
to consumers that are loaded thereon by an automated loading
system; wherein the control circuit is further configured to:
receive identification information from the bin sensor regarding
unwanted suggested retail items placed in the rejection bin and to
update the one or more partialities in the database of user
profiles based, in part, on the particular customer associated with
the user profile placing one of the unwanted suggested retail items
in the rejection bin; and receive a product information query from
a user computing device, identify a set of products having product
attributes corresponding to the product information query received,
and instruct the product support system to physically present at
least one product of the identified set of products by loading the
at least one product of the identified set onto the product support
system by one or more retrieval devices of the automated loading
system, for physical presentation to the particular customer at the
retail shopping facility.
2. The system of claim 1 further comprising a staging area within
the retail shopping facility with numerous shopping carts
designated for use by particular customers, the designated shopping
carts filled with at least one of the one or more suggested retail
items for the particular customers, the staging area being where
the particular customers retrieve their designated shopping
carts.
3. The system of claim 1 wherein the user profile has purchased
retail products associated therewith and at least one identified
partiality associated with the purchased retail products.
4. The system of claim 1 further comprising one or more point of
sale terminals and the control circuit is further configured to
update the user profile associated with the particular customer
based, in part, on retail products purchased by the particular
customer at the point of sale terminal and the identified
vectorized product characterizations associated with the purchased
retail products.
5. The system of claim 1 further comprising a user computing device
and wherein the control circuit is configured to receive, from the
user computing device associated with the particular customer, a
collection time and a collection location for picking up the
particular customer's designated shopping cart filled with the at
least one of the one or more suggested retail items.
6. The system of claim 1 further comprising a user computing device
and wherein the control circuit receives location information from
the user computing device associated with the particular customer
and the at least one of the one or more suggested retail items are
loaded into the designated shopping cart as the particular customer
is identified as approaching approaches the retail shopping
facility.
7. The system of claim 1 wherein the database of user profiles
further includes a purchase history and the control circuit is
further configured to identify the one or more suggested retail
items based, in part, on the purchase history of the particular
customer.
8. The system of claim 1 wherein the control circuit is configured
to update the database of user profiles according to purchases at
multiple retail facilities.
9. The system of claim 1 wherein the partialities are represented
by partiality vectors and can include values, preferences, and
affinities.
10. The system of claim 9 wherein the control circuit is further
configured to analyze the partiality vectors and the vectorized
product characterizations and identify an overlap therebetween.
11. The system of claim 1 wherein the control circuit is further
configured to select the at least one product of the identified set
of products for physical presentation based, in part, on the user
profile associated with the particular customer submitting the
product information query.
12. A method comprising: maintaining a database of a plurality of
user profiles having one or more identified partialities associated
therewith and the user profiles being associated with particular
customers; maintaining a database of retail products, at least some
of the retail products having identified vectorized product
characterizations; identifying, via a control circuit, one or more
suggested retail items for a particular customer based, in part, on
comparisons between the identified partialities of the user profile
associated with the particular customer and the identified
vectorized product characterizations of the retail products;
loading a designated shopping cart, at a retail shopping facility,
for the particular customer with at least one of the one or more
suggested retail items prior to the particular customer arriving at
the retail shopping facility; receiving unwanted suggested retail
items deposited into a rejection bin, the rejection bin having a
bin sensor configured to detect placement of the unwanted suggest
retail items and to notify the control circuit; receiving, at the
control circuit, identification information regarding the unwanted
suggested retail items and updating the one or more partialities in
the database of user profiles based, in part, on the particular
customer associated with the user profile placing one of the
unwanted suggested retail items in the rejection bin; receiving a
product information query at the control circuit from a user
computing device; identifying, via the control circuit, a set of
products having product attributes corresponding to the received
product information query; instructing a product support system and
a product routing system communicatively coupled to the control
circuit to physically present at least one product of the
identified set of products; and physically presenting the at least
one product of the identified set of products to the particular
customer on the product support system at the retail shopping
facility wherein the at least one product of the identified set of
products is loaded onto the product support system by one or more
retrieval devices of an automated loading system or physically
presenting the at least one product of the identified set of
products to the particular customer on the product support system
at the retail shopping facility wherein the at least one product of
the identified set of products is loaded onto a display portion of
the product support system that is transported to the particular
customer at the retail shopping facility by one or more conveyors
of the product routing system.
13. The method of claim 12 further comprising providing the
designated shopping cart at a staging area within the retail
shopping facility.
14. The method of claim 12 further comprising updating the user
profile based, in part, on information received about the
particular customer from a point of sale terminal visited by the
particular customer before exiting the retail shopping
facility.
15. The method of claim 12 further comprising receiving, from a
user computing device associated with the particular customer, a
collection time and a collection location for picking up the
particular customer's designated shopping cart filled with the one
or more suggested retail items.
16. The method of claim 12 further comprising receiving location
information from a user computing device associated with the
particular customer and loading the one or more suggested retail
items into the designated shopping cart as the particular customer
approaches the retail shopping facility.
17. The method of claim 9 further comprising: selecting the at
least one product of the identified set of products for physical
presentation based, in part, on the user profile associated with
the particular customer submitting the product information
query.
18. A system comprising: a database of user profiles, the user
profiles having one or more partialities associated therewith and
the user profiles being associated with particular customers; a
database of retail products, at least some of the retail products
having identified vectorized product characterizations; a control
circuit configured to access the database of user profiles and the
database of retail products and configured to identify one or more
suggested retail items for a particular customer based, in part, on
comparisons between the identified partialities of the user profile
associated with the particular customer and the identified
vectorized product characterizations of the retail products; a
plurality of physical shopping carts at a retail shopping facility,
one of the plurality of shopping carts being designated for the
particular customer and filled with at least one of the one or more
suggested retail items prior to the particular customer arriving at
the retail shopping facility; a rejection bin where the particular
customer may deposit unwanted suggested retail items, the rejection
bin having a bin sensor configured to detect placement of the
unwanted suggested retail items; and a product support system
configured to present products to consumers that are loaded onto a
display thereof; wherein the control circuit is further configured
to: receive identification information from the bin sensor
regarding unwanted suggested retail items placed in the rejection
bin and to update the one or more partialities in the database of
user profiles based, in part, on the particular customer associated
with the user profile placing one of the unwanted suggested retail
items in the rejection bin; and receive a product information query
from a user computing device, identify a set of products having
product attributes corresponding to the product information query
received, and instruct the product support system to physically
present at least one product of the identified set of products by
transporting by one or more conveyors of a product routing system,
the display of the product support system loaded with the at least
one product of the identified set to the particular customer at the
retail shopping facility for physical presentation of the at least
one product of the identified set.
Description
TECHNICAL FIELD
These teachings relate generally to providing products and services
to individuals.
BACKGROUND
Various shopping paradigms are known in the art. One approach of
long-standing use essentially comprises displaying a variety of
different goods at a shared physical location and allowing
consumers to view/experience those offerings as they wish to
thereby make their purchasing selections. This model is being
increasingly challenged due at least in part to the logistical and
temporal inefficiencies that accompany this approach and also
because this approach does not assure that a product best suited to
a particular consumer will in fact be available for that consumer
to purchase at the time of their visit.
Increasing efforts are being made to present a given consumer with
one or more purchasing options that are selected based upon some
preference of the consumer. Existing preference-based approaches
leave much to be desired. Information regarding preferences, for
example, may tend to be very product specific and accordingly may
have little value apart from use with a very specific product or
product category. As a result, while helpful, a preferences-based
approach is inherently very limited in scope and offers only a very
weak platform by which to assess a wide variety of product and
service categories.
BRIEF DESCRIPTION OF THE DRAWINGS
Disclosed herein are embodiments of systems, apparatuses and
methods pertaining to systems and methods for facilitating shopping
in a physical retail facility. This description includes drawings,
wherein:
FIG. 1 comprises a flow diagram as configured in accordance with
various embodiments of these teachings;
FIG. 2 comprises a flow diagram as configured in accordance with
various embodiments of these teachings;
FIG. 3 comprises a graphic representation as configured in
accordance with various embodiments of these teachings;
FIG. 4 comprises a graph as configured in accordance with various
embodiments of these teachings;
FIG. 5 comprises a flow diagram as configured in accordance with
various embodiments of these teachings;
FIG. 6 comprises a graphic representation as configured in
accordance with various embodiments of these teachings;
FIG. 7 comprises a graphic representation as configured in
accordance with various embodiments of these teachings;
FIG. 8 comprises a graphic representation as configured in
accordance with various embodiments of these teachings;
FIG. 9 comprises a flow diagram as configured in accordance with
various embodiments of these teachings;
FIG. 10 comprises a flow diagram as configured in accordance with
various embodiments of these teachings;
FIG. 11 comprises a graphic representation as configured in
accordance with various embodiments of these teachings;
FIG. 12 comprises a graphic representation as configured in
accordance with various embodiments of these teachings;
FIG. 13 comprises a block diagram as configured in accordance with
various embodiments of these teachings;
FIG. 14 comprises a flow diagram as configured in accordance with
various embodiments of these teachings;
FIG. 15 comprises a graph as configured in accordance with various
embodiments of these teachings;
FIG. 16 comprises a flow diagram as configured in accordance with
various embodiments of these teachings;
FIG. 17 comprises a block diagram as configured in accordance with
various embodiments of these teachings;
FIG. 18 is schematic diagram in accordance with some
embodiments;
FIG. 19 is a flow diagram in accordance with some embodiments;
FIG. 20 illustrates a simplified block diagram of a retail system,
in accordance with some embodiments;
FIG. 21 illustrates a simplified flow diagram of a process of
enhancing customers' retail shopping experiences, in accordance
with some embodiments;
FIG. 22 illustrates a simplified block diagram of an exemplary
retail system configured to physically present an assortment of
products to customers, in accordance with some embodiments;
FIG. 23 illustrates a simplified flow diagram of an exemplary
process of providing a customized shopping experience for customers
and presenting retail products to customers at a shopping facility,
in accordance with some embodiments;
FIG. 24 is a diagrammatic top view of a retail location in
accordance with some embodiments;
FIG. 25 is a diagrammatic view of a mobile communication device in
accordance with several embodiments;
FIG. 26 is a perspective view of a shelving unit and the mobile
communication device of FIG. 25 communicating with a computing
device over a network in accordance with some embodiments;
FIG. 27 is a flow diagram in accordance with several embodiments;
and
FIG. 28 illustrates an exemplary system for use in implementing
methods, techniques, devices, apparatuses, systems, servers,
sources and providing enhanced customer experiences, in accordance
with some embodiments.
Elements in the figures are illustrated for simplicity and clarity
and have not necessarily been drawn to scale. For example, the
dimensions and/or relative positioning of some of the elements in
the figures may be exaggerated relative to other elements to help
to improve understanding of various embodiments of the present
invention. Also, common but well-understood elements that are
useful or necessary in a commercially feasible embodiment are often
not depicted in order to facilitate a less obstructed view of these
various embodiments of the present invention. Certain actions
and/or steps may be described or depicted in a particular order of
occurrence while those skilled in the art will understand that such
specificity with respect to sequence is not actually required. The
terms and expressions used herein have the ordinary technical
meaning as is accorded to such terms and expressions by persons
skilled in the technical field as set forth above except where
different specific meanings have otherwise been set forth
herein.
DETAILED DESCRIPTION
Generally speaking, many of these embodiments provide for a memory
having information stored therein that includes partiality
information for each of a plurality of persons in the form of a
plurality of partiality vectors for each of the persons wherein
each partiality vector has at least one of a magnitude and an angle
that corresponds to a magnitude of the person's belief in an amount
of good that comes from an order associated with that partiality.
This memory can also contain vectorized characterizations for each
of a plurality of products, wherein each of the vectorized
characterizations includes a measure regarding an extent to which a
corresponding one of the products accords with a corresponding one
of the plurality of partiality vectors.
Rules can then be provided that use the aforementioned information
in support of a wide variety of activities and results. Although
the described vector-based approaches bear little resemblance (if
any) (conceptually or in practice) to prior approaches to
understanding and/or metricizing a given person's product/service
requirements, these approaches yield numerous benefits including,
at least in some cases, reduced memory requirements, an ability to
accommodate (both initially and dynamically over time) an
essentially endless number and variety of partialities and/or
product attributes, and processing/comparison capabilities that
greatly ease computational resource requirements and/or greatly
reduced time-to-solution results.
People tend to be partial to ordering various aspects of their
lives, which is to say, people are partial to having things well
arranged per their own personal view of how things should be. As a
result, anything that contributes to the proper ordering of things
regarding which a person has partialities represents value to that
person. Quite literally, improving order reduces entropy for the
corresponding person (i.e., a reduction in the measure of disorder
present in that particular aspect of that person's life) and that
improvement in order/reduction in disorder is typically viewed with
favor by the affected person.
Generally speaking a value proposition must be coherent (logically
sound) and have "force." Here, force takes the form of an
imperative. When the parties to the imperative have a reputation of
being trustworthy and the value proposition is perceived to yield a
good outcome, then the imperative becomes anchored in the center of
a belief that "this is something that I must do because the results
will be good for me." With the imperative so anchored, the
corresponding material space can be viewed as conforming to the
order specified in the proposition that will result in the good
outcome.
Pursuant to these teachings a belief in the good that comes from
imposing a certain order takes the form of a value proposition. It
is a set of coherent logical propositions by a trusted source that,
when taken together, coalesce to form an imperative that a person
has a personal obligation to order their lives because it will
return a good outcome which improves their quality of life. This
imperative is a value force that exerts the physical force (effort)
to impose the desired order. The inertial effects come from the
strength of the belief. The strength of the belief comes from the
force of the value argument (proposition). And the force of the
value proposition is a function of the perceived good and trust in
the source that convinced the person's belief system to order
material space accordingly. A belief remains constant until acted
upon by a new force of a trusted value argument. This is at least a
significant reason why the routine in people's lives remains
relatively constant.
Newton's three laws of motion have a very strong bearing on the
present teachings. Stated summarily, Newton's first law holds that
an object either remains at rest or continues to move at a constant
velocity unless acted upon by a force, the second law holds that
the vector sum of the forces F on an object equal the mass m of
that object multiplied by the acceleration a of the object (i.e.,
F=ma), and the third law holds that when one body exerts a force on
a second body, the second body simultaneously exerts a force equal
in magnitude and opposite in direction on the first body.
Relevant to both the present teachings and Newton's first law,
beliefs can be viewed as having inertia. In particular, once a
person believes that a particular order is good, they tend to
persist in maintaining that belief and resist moving away from that
belief. The stronger that belief the more force an argument and/or
fact will need to move that person away from that belief to a new
belief.
Relevant to both the present teachings and Newton's second law, the
"force" of a coherent argument can be viewed as equaling the "mass"
which is the perceived Newtonian effort to impose the order that
achieves the aforementioned belief in the good which an imposed
order brings multiplied by the change in the belief of the good
which comes from the imposition of that order. Consider that when a
change in the value of a particular order is observed then there
must have been a compelling value claim influencing that change.
There is a proportionality in that the greater the change the
stronger the value argument. If a person values a particular
activity and is very diligent to do that activity even when facing
great opposition, we say they are dedicated, passionate, and so
forth. If they stop doing the activity, it begs the question, what
made them stop? The answer to that question needs to carry enough
force to account for the change.
And relevant to both the present teachings and Newton's third law,
for every effort to impose good order there is an equal and
opposite good reaction.
FIG. 1 provides a simple illustrative example in these regards. At
block 101 it is understood that a particular person has a
partiality (to a greater or lesser extent) to a particular kind of
order. At block 102 that person willingly exerts effort to impose
that order to thereby, at block 103, achieve an arrangement to
which they are partial. And at block 104, this person appreciates
the "good" that comes from successfully imposing the order to which
they are partial, in effect establishing a positive feedback
loop.
Understanding these partialities to particular kinds of order can
be helpful to understanding how receptive a particular person may
be to purchasing a given product or service. FIG. 2 provides a
simple illustrative example in these regards. At block 201 it is
understood that a particular person values a particular kind of
order. At block 202 it is understood (or at least presumed) that
this person wishes to lower the effort (or is at least receptive to
lowering the effort) that they must personally exert to impose that
order. At decision block 203 (and with access to information 204
regarding relevant products and or services) a determination can be
made whether a particular product or service lowers the effort
required by this person to impose the desired order. When such is
not the case, it can be concluded that the person will not likely
purchase such a product/service 205 (presuming better choices are
available).
When the product or service does lower the effort required to
impose the desired order, however, at block 206 a determination can
be made as to whether the amount of the reduction of effort
justifies the cost of purchasing and/or using the proffered
product/service. If the cost does not justify the reduction of
effort, it can again be concluded that the person will not likely
purchase such a product/service 205. When the reduction of effort
does justify the cost, however, this person may be presumed to want
to purchase the product/service and thereby achieve the desired
order (or at least an improvement with respect to that order) with
less expenditure of their own personal effort (block 207) and
thereby achieve, at block 208, corresponding enjoyment or
appreciation of that result.
To facilitate such an analysis, the applicant has determined that
factors pertaining to a person's partialities can be quantified and
otherwise represented as corresponding vectors (where "vector" will
be understood to refer to a geometric object/quantity having both
an angle and a length/magnitude). These teachings will accommodate
a variety of differing bases for such partialities including, for
example, a person's values, affinities, aspirations, and
preferences.
A value is a person's principle or standard of behavior, their
judgment of what is important in life. A person's values represent
their ethics, moral code, or morals and not a mere unprincipled
liking or disliking of something. A person's value might be a
belief in kind treatment of animals, a belief in cleanliness, a
belief in the importance of personal care, and so forth.
An affinity is an attraction (or even a feeling of kinship) to a
particular thing or activity. Examples including such a feeling
towards a participatory sport such as golf or a spectator sport
(including perhaps especially a particular team such as a
particular professional or college football team), a hobby (such as
quilting, model railroading, and so forth), one or more components
of popular culture (such as a particular movie or television
series, a genre of music or a particular musical performance group,
or a given celebrity, for example), and so forth.
"Aspirations" refer to longer-range goals that require months or
even years to reasonably achieve. As used herein "aspirations" does
not include mere short term goals (such as making a particular meal
tonight or driving to the store and back without a vehicular
incident). The aspired-to goals, in turn, are goals pertaining to a
marked elevation in one's core competencies (such as an aspiration
to master a particular game such as chess, to achieve a particular
articulated and recognized level of martial arts proficiency, or to
attain a particular articulated and recognized level of cooking
proficiency), professional status (such as an aspiration to receive
a particular advanced education degree, to pass a professional
examination such as a state Bar examination of a Certified Public
Accountants examination, or to become Board certified in a
particular area of medical practice), or life experience milestone
(such as an aspiration to climb Mount Everest, to visit every state
capital, or to attend a game at every major league baseball park in
the United States). It will further be understood that the goal(s)
of an aspiration is not something that can likely merely simply
happen of its own accord; achieving an aspiration requires an
intelligent effort to order one's life in a way that increases the
likelihood of actually achieving the corresponding goal or goals to
which that person aspires. One aspires to one day run their own
business as versus, for example, merely hoping to one day win the
state lottery.
A preference is a greater liking for one alternative over another
or others. A person can prefer, for example, that their steak is
cooked "medium" rather than other alternatives such as "rare" or
"well done" or a person can prefer to play golf in the morning
rather than in the afternoon or evening. Preferences can and do
come into play when a given person makes purchasing decisions at a
retail shopping facility. Preferences in these regards can take the
form of a preference for a particular brand over other available
brands or a preference for economy-sized packaging as versus, say,
individual serving-sized packaging.
Values, affinities, aspirations, and preferences are not
necessarily wholly unrelated. It is possible for a person's values,
affinities, or aspirations to influence or even dictate their
preferences in specific regards. For example, a person's moral code
that values non-exploitive treatment of animals may lead them to
prefer foods that include no animal-based ingredients and hence to
prefer fruits and vegetables over beef and chicken offerings. As
another example, a person's affinity for a particular musical group
may lead them to prefer clothing that directly or indirectly
references or otherwise represents their affinity for that group.
As yet another example, a person's aspirations to become a
Certified Public Accountant may lead them to prefer
business-related media content.
While a value, affinity, or aspiration may give rise to or
otherwise influence one or more corresponding preferences, however,
is not to say that these things are all one and the same; they are
not. For example, a preference may represent either a principled or
an unprincipled liking for one thing over another, while a value is
the principle itself. Accordingly, as used herein it will be
understood that a partiality can include, in context, any one or
more of a value-based, affinity-based, aspiration-based, and/or
preference-based partiality unless one or more such features is
specifically excluded per the needs of a given application
setting.
Information regarding a given person's partialities can be acquired
using any one or more of a variety of information-gathering and/or
analytical approaches. By one simple approach, a person may
voluntarily disclose information regarding their partialities (for
example, in response to an online questionnaire or survey or as
part of their social media presence). By another approach, the
purchasing history for a given person can be analyzed to intuit the
partialities that led to at least some of those purchases. By yet
another approach demographic information regarding a particular
person can serve as yet another source that sheds light on their
partialities. Other ways that people reveal how they order their
lives include but are not limited to: (1) their social networking
profiles and behaviors (such as the things they "like" via
Facebook, the images they post via Pinterest, informal and formal
comments they initiate or otherwise provide in response to
third-party postings including statements regarding their own
personal long-term goals, the persons/topics they follow via
Twitter, the photographs they publish via Picasso, and so forth);
(2) their Internet surfing history; (3) their on-line or
otherwise-published affinity-based memberships; (4) real-time (or
delayed) information (such as steps walked, calories burned,
geographic location, activities experienced, and so forth) from any
of a variety of personal sensors (such as smart phones,
tablet/pad-styled computers, fitness wearables, Global Positioning
System devices, and so forth) and the so-called Internet of Things
(such as smart refrigerators and pantries, entertainment and
information platforms, exercise and sporting equipment, and so
forth); (5) instructions, selections, and other inputs (including
inputs that occur within augmented-reality user environments) made
by a person via any of a variety of interactive interfaces (such as
keyboards and cursor control devices, voice recognition,
gesture-based controls, and eye tracking-based controls), and so
forth.
The present teachings employ a vector-based approach to facilitate
characterizing, representing, understanding, and leveraging such
partialities to thereby identify products (and/or services) that
will, for a particular corresponding consumer, provide for an
improved or at least a favorable corresponding ordering for that
consumer. Vectors are directed quantities that each have both a
magnitude and a direction. Per the applicant's approach these
vectors have a real, as versus a metaphorical, meaning in the sense
of Newtonian physics. Generally speaking, each vector represents
order imposed upon material space-time by a particular
partiality.
FIG. 3 provides some illustrative examples in these regards. By one
approach the vector 300 has a corresponding magnitude 301 (i.e.,
length) that represents the magnitude of the strength of the belief
in the good that comes from that imposed order (which belief, in
turn, can be a function, relatively speaking, of the extent to
which the order for this particular partiality is enabled and/or
achieved). In this case, the greater the magnitude 301, the greater
the strength of that belief and vice versa. Per another example,
the vector 300 has a corresponding angle A 302 that instead
represents the foregoing magnitude of the strength of the belief
(and where, for example, an angle of 0.degree. represents no such
belief and an angle of 90.degree. represents a highest magnitude in
these regards, with other ranges being possible as desired).
Accordingly, a vector serving as a partiality vector can have at
least one of a magnitude and an angle that corresponds to a
magnitude of a particular person's belief in an amount of good that
comes from an order associated with a particular partiality.
Applying force to displace an object with mass in the direction of
a certain partiality-based order creates worth for a person who has
that partiality. The resultant work (i.e., that force multiplied by
the distance the object moves) can be viewed as a worth vector
having a magnitude equal to the accomplished work and having a
direction that represents the corresponding imposed order. If the
resultant displacement results in more order of the kind that the
person is partial to then the net result is a notion of "good."
This "good" is a real quantity that exists in meta-physical space
much like work is a real quantity in material space. The link
between the "good" in meta-physical space and the work in material
space is that it takes work to impose order that has value.
In the context of a person, this effort can represent, quite
literally, the effort that the person is willing to exert to be
compliant with (or to otherwise serve) this particular partiality.
For example, a person who values animal rights would have a large
magnitude worth vector for this value if they exerted considerable
physical effort towards this cause by, for example, volunteering at
animal shelters or by attending protests of animal cruelty.
While these teachings will readily employ a direct measurement of
effort such as work done or time spent, these teachings will also
accommodate using an indirect measurement of effort such as
expense; in particular, money. In many cases people trade their
direct labor for payment. The labor may be manual or intellectual.
While salaries and payments can vary significantly from one person
to another, a same sense of effort applies at least in a relative
sense.
As a very specific example in these regards, there are wristwatches
that require a skilled craftsman over a year to make. The actual
aggregated amount of force applied to displace the small components
that comprise the wristwatch would be relatively very small. That
said, the skilled craftsman acquired the necessary skill to so
assemble the wristwatch over many years of applying force to
displace thousands of little parts when assembly previous
wristwatches. That experience, based upon a much larger aggregation
of previously-exerted effort, represents a genuine part of the
"effort" to make this particular wristwatch and hence is fairly
considered as part of the wristwatch's worth.
The conventional forces working in each person's mind are typically
more-or-less constantly evaluating the value propositions that
correspond to a path of least effort to thereby order their lives
towards the things they value. A key reason that happens is because
the actual ordering occurs in material space and people must exert
real energy in pursuit of their desired ordering. People therefore
naturally try to find the path with the least real energy expended
that still moves them to the valued order. Accordingly, a trusted
value proposition that offers a reduction of real energy will be
embraced as being "good" because people will tend to be partial to
anything that lowers the real energy they are required to exert
while remaining consistent with their partialities.
FIG. 4 presents a space graph that illustrates many of the
foregoing points. A first vector 401 represents the time required
to make such a wristwatch while a second vector 402 represents the
order associated with such a device (in this case, that order
essentially represents the skill of the craftsman). These two
vectors 401 and 402 in turn sum to form a third vector 403 that
constitutes a value vector for this wristwatch. This value vector
403, in turn, is offset with respect to energy (i.e., the energy
associated with manufacturing the wristwatch).
A person partial to precision and/or to physically presenting an
appearance of success and status (and who presumably has the
wherewithal) may, in turn, be willing to spend $100,000 for such a
wristwatch. A person able to afford such a price, of course, may
themselves be skilled at imposing a certain kind of order that
other persons are partial to such that the amount of physical work
represented by each spent dollar is small relative to an amount of
dollars they receive when exercising their skill(s). (Viewed
another way, wearing an expensive wristwatch may lower the effort
required for such a person to communicate that their own personal
success comes from being highly skilled in a certain order of high
worth.)
Generally speaking, all worth comes from imposing order on the
material space-time. The worth of a particular order generally
increases as the skill required to impose the order increases.
Accordingly, unskilled labor may exchange $10 for every hour worked
where the work has a high content of unskilled physical labor while
a highly-skilled data scientist may exchange $75 for every hour
worked with very little accompanying physical effort.
Consider a simple example where both of these laborers are partial
to a well-ordered lawn and both have a corresponding partiality
vector in those regards with a same magnitude. To observe that
partiality the unskilled laborer may own an inexpensive push power
lawn mower that this person utilizes for an hour to mow their lawn.
The data scientist, on the other hand, pays someone else $75 in
this example to mow their lawn. In both cases these two individuals
traded one hour of worth creation to gain the same worth (to them)
in the form of a well-ordered lawn; the unskilled laborer in the
form of direct physical labor and the data scientist in the form of
money that required one hour of their specialized effort to
earn.
This same vector-based approach can also represent various products
and services. This is because products and services have worth (or
not) because they can remove effort (or fail to remove effort) out
of the customer's life in the direction of the order to which the
customer is partial. In particular, a product has a perceived
effort embedded into each dollar of cost in the same way that the
customer has an amount of perceived effort embedded into each
dollar earned. A customer has an increased likelihood of responding
to an exchange of value if the vectors for the product and the
customer's partiality are directionally aligned and where the
magnitude of the vector as represented in monetary cost is somewhat
greater than the worth embedded in the customer's dollar.
Put simply, the magnitude (and/or angle) of a partiality vector for
a person can represent, directly or indirectly, a corresponding
effort the person is willing to exert to pursue that partiality.
There are various ways by which that value can be determined. As
but one non-limiting example in these regards, the magnitude/angle
V of a particular partiality vector can be expressed as:
.function..times..times..times..times. ##EQU00001## where X refers
to any of a variety of inputs (such as those described above) that
can impact the characterization of a particular partiality (and
where these teachings will accommodate either or both subjective
and objective inputs as desired) and W refers to weighting factors
that are appropriately applied the foregoing input values (and
where, for example, these weighting factors can have values that
themselves reflect a particular person's consumer personality or
otherwise as desired and can be static or dynamically valued in
practice as desired).
In the context of a product (or service) the magnitude/angle of the
corresponding vector can represent the reduction of effort that
must be exerted when making use of this product to pursue that
partiality, the effort that was expended in order to create the
product/service, the effort that the person perceives can be
personally saved while nevertheless promoting the desired order,
and/or some other corresponding effort. Taken as a whole the sum of
all the vectors must be perceived to increase the overall order to
be considered a good product/service.
It may be noted that while reducing effort provides a very useful
metric in these regards, it does not necessarily follow that a
given person will always gravitate to that which most reduces
effort in their life. This is at least because a given person's
values (for example) will establish a baseline against which a
person may eschew some goods/services that might in fact lead to a
greater overall reduction of effort but which would conflict,
perhaps fundamentally, with their values. As a simple illustrative
example, a given person might value physical activity. Such a
person could experience reduced effort (including effort
represented via monetary costs) by simply sitting on their couch,
but instead will pursue activities that involve that valued
physical activity. That said, however, the goods and services that
such a person might acquire in support of their physical activities
are still likely to represent increased order in the form of
reduced effort where that makes sense. For example, a person who
favors rock climbing might also favor rock climbing clothing and
supplies that render that activity safer to thereby reduce the
effort required to prevent disorder as a consequence of a fall (and
consequently increasing the good outcome of the rock climber's
quality experience).
By forming reliable partiality vectors for various individuals and
corresponding product characterization vectors for a variety of
products and/or services, these teachings provide a useful and
reliable way to identify products/services that accord with a given
person's own partialities (whether those partialities are based on
their values, their affinities, their preferences, or
otherwise).
It is of course possible that partiality vectors may not be
available yet for a given person due to a lack of sufficient
specific source information from or regarding that person. In this
case it may nevertheless be possible to use one or more partiality
vector templates that generally represent certain groups of people
that fairly include this particular person. For example, if the
person's gender, age, academic status/achievements, and/or postal
code are known it may be useful to utilize a template that includes
one or more partiality vectors that represent some statistical
average or norm of other persons matching those same characterizing
parameters, see, e.g., step 509 in FIG. 5. (Of course, while it may
be useful to at least begin to employ these teachings with certain
individuals by using one or more such templates, these teachings
will also accommodate modifying (perhaps significantly and perhaps
quickly) such a starting point over time as part of developing a
more personal set of partiality vectors that are specific to the
individual.) A variety of templates could be developed based, for
example, on professions, academic pursuits and achievements,
nationalities and/or ethnicities, characterizing hobbies, and the
like.
FIG. 5 presents a process 500 that illustrates yet another approach
in these regards. For the sake of an illustrative example it will
be presumed here that a control circuit of choice (with useful
examples in these regards being presented further below) carries
out one or more of the described steps/actions.
At block 501 the control circuit monitors a person's behavior over
time. The range of monitored behaviors can vary with the individual
and the application setting. By one approach, only behaviors that
the person has specifically approved for monitoring are so
monitored.
As one example in these regards, this monitoring can be based, in
whole or in part, upon interaction records 502 that reflect or
otherwise track, for example, the monitored person's purchases.
This can include specific items purchased by the person, from whom
the items were purchased, where the items were purchased, how the
items were purchased (for example, at a bricks-and-mortar physical
retail shopping facility or via an on-line shopping opportunity),
the price paid for the items, and/or which items were returned and
when), and so forth.
As another example in these regards the interaction records 502 can
pertain to the social networking behaviors of the monitored person
including such things as their "likes," their posted comments,
images, and tweets, affinity group affiliations, their on-line
profiles, their playlists and other indicated "favorites," and so
forth. Such information can sometimes comprise a direct indication
of a particular partiality or, in other cases, can indirectly point
towards a particular partiality and/or indicate a relative strength
of the person's partiality.
Other interaction records of potential interest include but are not
limited to registered political affiliations and activities, credit
reports, military-service history, educational and employment
history, and so forth.
As another example, in lieu of the foregoing or in combination
therewith, this monitoring can be based, in whole or in part, upon
sensor inputs from the Internet of Things (IOT) 503. The Internet
of Things refers to the Internet-based inter-working of a wide
variety of physical devices including but not limited to wearable
or carriable devices, vehicles, buildings, and other items that are
embedded with electronics, software, sensors, network connectivity,
and sometimes actuators that enable these objects to collect and
exchange data via the Internet. In particular, the Internet of
Things allows people and objects pertaining to people to be sensed
and corresponding information to be transferred to remote locations
via intervening network infrastructure. Some experts estimate that
the Internet of Things will consist of almost 50 billion such
objects by 2020. (Further description in these regards appears
further herein.)
Depending upon what sensors a person encounters, information can be
available regarding a person's travels, lifestyle, calorie
expenditure over time, diet, habits, interests and affinities,
choices and assumed risks, and so forth. This process 500 will
accommodate either or both real-time or non-real time access to
such information as well as either or both push and pull-based
paradigms.
By monitoring a person's behavior over time a general sense of that
person's daily routine can be established (sometimes referred to
herein as a routine experiential base state). As a very simple
illustrative example, a routine experiential base state can include
a typical daily event timeline for the person that represents
typical locations that the person visits and/or typical activities
in which the person engages. The timeline can indicate those
activities that tend to be scheduled (such as the person's time at
their place of employment or their time spent at their child's
sports practices) as well as visits/activities that are normal for
the person though not necessarily undertaken with strict observance
to a corresponding schedule (such as visits to local stores, movie
theaters, and the homes of nearby friends and relatives).
At block 504 this process 500 provides for detecting changes to
that established routine. These teachings are highly flexible in
these regards and will accommodate a wide variety of "changes."
Some illustrative examples include but are not limited to changes
with respect to a person's travel schedule, destinations visited or
time spent at a particular destination, the purchase and/or use of
new and/or different products or services, a subscription to a new
magazine, a new Rich Site Summary (RSS) feed or a subscription to a
new blog, a new "friend" or "connection" on a social networking
site, a new person, entity, or cause to follow on a Twitter-like
social networking service, enrollment in an academic program, and
so forth.
Upon detecting a change, at optional block 505 this process 500
will accommodate assessing whether the detected change constitutes
a sufficient amount of data to warrant proceeding further with the
process. This assessment can comprise, for example, assessing
whether a sufficient number (i.e., a predetermined number) of
instances of this particular detected change have occurred over
some predetermined period of time. As another example, this
assessment can comprise assessing whether the specific details of
the detected change are sufficient in quantity and/or quality to
warrant further processing. For example, merely detecting that the
person has not arrived at their usual 6 PM-Wednesday dance class
may not be enough information, in and of itself, to warrant further
processing, in which case the information regarding the detected
change may be discarded or, in the alternative, cached for further
consideration and use in conjunction or aggregation with other,
later-detected changes.
At block 506 this process 500 uses these detected changes to create
a spectral profile for the monitored person. FIG. 6 provides an
illustrative example in these regards with the spectral profile
denoted by reference numeral 601. In this illustrative example the
spectral profile 601 represents changes to the person's behavior
over a given period of time (such as an hour, a day, a week, or
some other temporal window of choice). Such a spectral profile can
be as multidimensional as may suit the needs of a given application
setting.
At optional block 507 this process 500 then provides for
determining whether there is a statistically significant
correlation between the aforementioned spectral profile and any of
a plurality of like characterizations 508. The like
characterizations 508 can comprise, for example, spectral profiles
that represent an average of groupings of people who share many of
the same (or all of the same) identified partialities. As a very
simple illustrative example in these regards, a first such
characterization 602 might represent a composite view of a first
group of people who have three similar partialities but a
dissimilar fourth partiality while another of the characterizations
603 might represent a composite view of a different group of people
who share all four partialities.
The aforementioned "statistically significant" standard can be
selected and/or adjusted to suit the needs of a given application
setting. The scale or units by which this measurement can be
assessed can be any known, relevant scale/unit including, but not
limited to, scales such as standard deviations, cumulative
percentages, percentile equivalents, Z-scores, T-scores, standard
nines, and percentages in standard nines. Similarly, the threshold
by which the level of statistical significance is measured/assessed
can be set and selected as desired. By one approach the threshold
is static such that the same threshold is employed regardless of
the circumstances. By another approach the threshold is dynamic and
can vary with such things as the relative size of the population of
people upon which each of the characterizations 508 are based
and/or the amount of data and/or the duration of time over which
data is available for the monitored person.
Referring now to FIG. 7, by one approach the selected
characterization (denoted by reference numeral 701 in this figure)
comprises an activity profile over time of one or more human
behaviors. Examples of behaviors include but are not limited to
such things as repeated purchases over time of particular
commodities, repeated visits over time to particular locales such
as certain restaurants, retail outlets, athletic or entertainment
facilities, and so forth, and repeated activities over time such as
floor cleaning, dish washing, car cleaning, cooking, volunteering,
and so forth. Those skilled in the art will understand and
appreciate, however, that the selected characterization is not, in
and of itself, demographic data (as described elsewhere
herein).
More particularly, the characterization 701 can represent (in this
example, for a plurality of different behaviors) each instance over
the monitored/sampled period of time when the monitored/represented
person engages in a particular represented behavior (such as
visiting a neighborhood gym, purchasing a particular product (such
as a consumable perishable or a cleaning product), interacts with a
particular affinity group via social networking, and so forth). The
relevant overall time frame can be chosen as desired and can range
in a typical application setting from a few hours or one day to
many days, weeks, or even months or years. (It will be understood
by those skilled in the art that the particular characterization
shown in FIG. 7 is intended to serve an illustrative purpose and
does not necessarily represent or mimic any particular behavior or
set of behaviors).
Generally speaking it is anticipated that many behaviors of
interest will occur at regular or somewhat regular intervals and
hence will have a corresponding frequency or periodicity of
occurrence. For some behaviors that frequency of occurrence may be
relatively often (for example, oral hygiene events that occur at
least once, and often multiple times each day) while other
behaviors (such as the preparation of a holiday meal) may occur
much less frequently (such as only once, or only a few times, each
year). For at least some behaviors of interest that general (or
specific) frequency of occurrence can serve as a significant
indication of a person's corresponding partialities.
By one approach, these teachings will accommodate detecting and
timestamping each and every event/activity/behavior or interest as
it happens. Such an approach can be memory intensive and require
considerable supporting infrastructure.
The present teachings will also accommodate, however, using any of
a variety of sampling periods in these regards. In some cases, for
example, the sampling period per se may be one week in duration. In
that case, it may be sufficient to know that the monitored person
engaged in a particular activity (such as cleaning their car) a
certain number of times during that week without known precisely
when, during that week, the activity occurred. In other cases it
may be appropriate or even desirable, to provide greater
granularity in these regards. For example, it may be better to know
which days the person engaged in the particular activity or even
the particular hour of the day. Depending upon the selected
granularity/resolution, selecting an appropriate sampling window
can help reduce data storage requirements (and/or corresponding
analysis/processing overhead requirements).
Although a given person's behaviors may not, strictly speaking, be
continuous waves (as shown in FIG. 7) in the same sense as, for
example, a radio or acoustic wave, it will nevertheless be
understood that such a behavioral characterization 701 can itself
be broken down into a plurality of sub-waves 702 that, when summed
together, equal or at least approximate to some satisfactory degree
the behavioral characterization 701 itself (The more-discrete and
sometimes less-rigidly periodic nature of the monitored behaviors
may introduce a certain amount of error into the corresponding
sub-waves. There are various mathematically satisfactory ways by
which such error can be accommodated including by use of weighting
factors and/or expressed tolerances that correspond to the
resultant sub-waves.)
It should also be understood that each such sub-wave can often
itself be associated with one or more corresponding discrete
partialities. For example, a partiality reflecting concern for the
environment may, in turn, influence many of the included behavioral
events (whether they are similar or dissimilar behaviors or not)
and accordingly may, as a sub-wave, comprise a relatively
significant contributing factor to the overall set of behaviors as
monitored over time. These sub-waves (partialities) can in turn be
clearly revealed and presented by employing a transform (such as a
Fourier transform) of choice to yield a spectral profile 703
wherein the X axis represents frequency and the Y axis represents
the magnitude of the response of the monitored person at each
frequency/sub-wave of interest.
This spectral response of a given individual--which is generated
from a time series of events that reflect/track that person's
behavior--yields frequency response characteristics for that person
that are analogous to the frequency response characteristics of
physical systems such as, for example, an analog or digital filter
or a second order electrical or mechanical system. Referring to
FIG. 8, for many people the spectral profile of the individual
person will exhibit a primary frequency 801 for which the greatest
response (perhaps many orders of magnitude greater than other
evident frequencies) to life is exhibited and apparent. In
addition, the spectral profile may also possibly identify one or
more secondary frequencies 802 above and/or below that primary
frequency 801. (It may be useful in many application settings to
filter out more distant frequencies 803 having considerably lower
magnitudes because of a reduced likelihood of relevance and/or
because of a possibility of error in those regards; in effect,
these lower-magnitude signals constitute noise that such filtering
can remove from consideration.)
As noted above, the present teachings will accommodate using
sampling windows of varying size. By one approach the frequency of
events that correspond to a particular partiality can serve as a
basis for selecting a particular sampling rate to use when
monitoring for such events. For example, Nyquist-based sampling
rules (which dictate sampling at a rate at least twice that of the
frequency of the signal of interest) can lead one to choose a
particular sampling rate (and the resultant corresponding sampling
window size).
As a simple illustration, if the activity of interest occurs only
once a week, then using a sampling of half-a-week and sampling
twice during the course of a given week will adequately capture the
monitored event. If the monitored person's behavior should change,
a corresponding change can be automatically made. For example, if
the person in the foregoing example begins to engage in the
specified activity three times a week, the sampling rate can be
switched to six times per week (in conjunction with a sampling
window that is resized accordingly).
By one approach, the sampling rate can be selected and used on a
partiality-by-partiality basis. This approach can be especially
useful when different monitoring modalities are employed to monitor
events that correspond to different partialities. If desired,
however, a single sampling rate can be employed and used for a
plurality (or even all) partialities/behaviors. In that case, it
can be useful to identify the behavior that is exemplified most
often (i.e., that behavior which has the highest frequency) and
then select a sampling rate that is at least twice that rate of
behavioral realization, as that sampling rate will serve well and
suffice for both that highest-frequency behavior and all
lower-frequency behaviors as well.
It can be useful in many application settings to assume that the
foregoing spectral profile of a given person is an inherent and
inertial characteristic of that person and that this spectral
profile, in essence, provides a personality profile of that person
that reflects not only how but why this person responds to a
variety of life experiences. More importantly, the partialities
expressed by the spectral profile for a given person will tend to
persist going forward and will not typically change significantly
in the absence of some powerful external influence (including but
not limited to significant life events such as, for example,
marriage, children, loss of job, promotion, and so forth).
In any event, by knowing a priori the particular partialities (and
corresponding strengths) that underlie the particular
characterization 701, those partialities can be used as an initial
template for a person whose own behaviors permit the selection of
that particular characterization 701. In particular, those
particularities can be used, at least initially, for a person for
whom an amount of data is not otherwise available to construct a
similarly rich set of partiality information.
As a very specific and non-limiting example, per these teachings
the choice to make a particular product can include consideration
of one or more value systems of potential customers. When
considering persons who value animal rights, a product conceived to
cater to that value proposition may require a corresponding
exertion of additional effort to order material space-time such
that the product is made in a way that (A) does not harm animals
and/or (even better) (B) improves life for animals (for example,
eggs obtained from free range chickens). The reason a person exerts
effort to order material space-time is because they believe it is
good to do and/or not good to not do so. When a person exerts
effort to do good (per their personal standard of "good") and if
that person believes that a particular order in material space-time
(that includes the purchase of a particular product) is good to
achieve, then that person will also believe that it is good to buy
as much of that particular product (in order to achieve that good
order) as their finances and needs reasonably permit (all other
things being equal).
The aforementioned additional effort to provide such a product can
(typically) convert to a premium that adds to the price of that
product. A customer who puts out extra effort in their life to
value animal rights will typically be willing to pay that extra
premium to cover that additional effort exerted by the company. By
one approach a magnitude that corresponds to the additional effort
exerted by the company can be added to the person's corresponding
value vector because a product or service has worth to the extent
that the product/service allows a person to order material
space-time in accordance with their own personal value system while
allowing that person to exert less of their own effort in direct
support of that value (since money is a scalar form of effort).
By one approach there can be hundreds or even thousands of
identified partialities. In this case, if desired, each
product/service of interest can be assessed with respect to each
and every one of these partialities and a corresponding partiality
vector formed to thereby build a collection of partiality vectors
that collectively characterize the product/service. As a very
simple example in these regards, a given laundry detergent might
have a cleanliness partiality vector with a relatively high
magnitude (representing the effectiveness of the detergent), a
ecology partiality vector that might be relatively low or possibly
even having a negative magnitude (representing an ecologically
disadvantageous effect of the detergent post usage due to increased
disorder in the environment), and a simple-life partiality vector
with only a modest magnitude (representing the relative ease of use
of the detergent but also that the detergent presupposes that the
user has a modern washing machine). Other partiality vectors for
this detergent, representing such things as nutrition or mental
acuity, might have magnitudes of zero.
As mentioned above, these teachings can accommodate partiality
vectors having a negative magnitude. Consider, for example, a
partiality vector representing a desire to order things to reduce
one's so-called carbon footprint. A magnitude of zero for this
vector would indicate a completely neutral effect with respect to
carbon emissions while any positive-valued magnitudes would
represent a net reduction in the amount of carbon in the
atmosphere, hence increasing the ability of the environment to be
ordered. Negative magnitudes would represent the introduction of
carbon emissions that increases disorder of the environment (for
example, as a result of manufacturing the product, transporting the
product, and/or using the product).
FIG. 9 presents one non-limiting illustrative example in these
regards. The illustrated process presumes the availability of a
library 901 of correlated relationships between product/service
claims and particular imposed orders. Examples of product/service
claims include such things as claims that a particular product
results in cleaner laundry or household surfaces, or that a
particular product is made in a particular political region (such
as a particular state or country), or that a particular product is
better for the environment, and so forth. The imposed orders to
which such claims are correlated can reflect orders as described
above that pertain to corresponding partialities.
At block 902 this process provides for decoding one or more
partiality propositions from specific product packaging (or service
claims). For example, the particular textual/graphics-based claims
presented on the packaging of a given product can be used to access
the aforementioned library 901 to identify one or more
corresponding imposed orders from which one or more corresponding
partialities can then be identified.
At block 903 this process provides for evaluating the
trustworthiness of the aforementioned claims. This evaluation can
be based upon any one or more of a variety of data points as
desired. FIG. 9 illustrates four significant possibilities in these
regards. For example, at block 904 an actual or estimated research
and development effort can be quantified for each claim pertaining
to a partiality. At block 905 an actual or estimated component
sourcing effort for the product in question can be quantified for
each claim pertaining to a partiality. At block 906 an actual or
estimated manufacturing effort for the product in question can be
quantified for each claim pertaining to a partiality. And at block
907 an actual or estimated merchandising effort for the product in
question can be quantified for each claim pertaining to a
partiality.
If desired, a product claim lacking sufficient trustworthiness may
simply be excluded from further consideration. By another approach
the product claim can remain in play but a lack of trustworthiness
can be reflected, for example, in a corresponding partiality vector
direction or magnitude for this particular product.
At block 908 this process provides for assigning an effort
magnitude for each evaluated product/service claim. That effort can
constitute a one-dimensional effort (reflecting, for example, only
the manufacturing effort) or can constitute a multidimensional
effort that reflects, for example, various categories of effort
such as the aforementioned research and development effort,
component sourcing effort, manufacturing effort, and so forth.
At block 909 this process provides for identifying a cost component
of each claim, this cost component representing a monetary value.
At block 910 this process can use the foregoing information with a
product/service partiality propositions vector engine to generate a
library 911 of one or more corresponding partiality vectors for the
processed products/services. Such a library can then be used as
described herein in conjunction with partiality vector information
for various persons to identify, for example, products/services
that are well aligned with the partialities of specific
individuals.
FIG. 10 provides another illustrative example in these same regards
and may be employed in lieu of the foregoing or in total or partial
combination therewith. Generally speaking, this process 1000 serves
to facilitate the formation of product characterization vectors for
each of a plurality of different products where the magnitude of
the vector length (and/or the vector angle) has a magnitude that
represents a reduction of exerted effort associated with the
corresponding product to pursue a corresponding user
partiality.
By one approach, and as illustrated in FIG. 10, this process 1000
can be carried out by a control circuit of choice. Specific
examples of control circuits are provided elsewhere herein.
As described further herein in detail, this process 1000 makes use
of information regarding various characterizations of a plurality
of different products. These teachings are highly flexible in
practice and will accommodate a wide variety of possible
information sources and types of information. By one optional
approach, and as shown at optional block 1001, the control circuit
can receive (for example, via a corresponding network interface of
choice) product characterization information from a third-party
product testing service. The magazine/web resource Consumers Report
provides one useful example in these regards. Such a resource
provides objective content based upon testing, evaluation, and
comparisons (and sometimes also provides subjective content
regarding such things as aesthetics, ease of use, and so forth) and
this content, provided as-is or pre-processed as desired, can
readily serve as useful third-party product testing service product
characterization information.
As another example, any of a variety of product-testing blogs that
are published on the Internet can be similarly accessed and the
product characterization information available at such resources
harvested and received by the control circuit. (The expression
"third party" will be understood to refer to an entity other than
the entity that operates/controls the control circuit and other
than the entity that provides the corresponding product
itself.)
As another example, and as illustrated at optional block 1002, the
control circuit can receive (again, for example, via a network
interface of choice) user-based product characterization
information. Examples in these regards include but are not limited
to user reviews provided on-line at various retail sites for
products offered for sale at such sites. The reviews can comprise
metricized content (for example, a rating expressed as a certain
number of stars out of a total available number of stars, such as 3
stars out of 5 possible stars) and/or text where the reviewers can
enter their objective and subjective information regarding their
observations and experiences with the reviewed products. In this
case, "user-based" will be understood to refer to users who are not
necessarily professional reviewers (though it is possible that
content from such persons may be included with the information
provided at such a resource) but who presumably purchased the
product being reviewed and who have personal experience with that
product that forms the basis of their review. By one approach the
resource that offers such content may constitute a third party as
defined above, but these teachings will also accommodate obtaining
such content from a resource operated or sponsored by the
enterprise that controls/operates this control circuit.
In any event, this process 1000 provides for accessing (see block
1004) information regarding various characterizations of each of a
plurality of different products. This information 1004 can be
gleaned as described above and/or can be obtained and/or developed
using other resources as desired. As one illustrative example in
these regards, the manufacturer and/or distributor of certain
products may source useful content in these regards.
These teachings will accommodate a wide variety of information
sources and types including both objective characterizing and/or
subjective characterizing information for the aforementioned
products.
Examples of objective characterizing information include, but are
not limited to, ingredients information (i.e., specific
components/materials from which the product is made), manufacturing
locale information (such as country of origin, state of origin,
municipality of origin, region of origin, and so forth), efficacy
information (such as metrics regarding the relative effectiveness
of the product to achieve a particular end-use result), cost
information (such as per product, per ounce, per application or
use, and so forth), availability information (such as present
in-store availability, on-hand inventory availability at a relevant
distribution center, likely or estimated shipping date, and so
forth), environmental impact information (regarding, for example,
the materials from which the product is made, one or more
manufacturing processes by which the product is made, environmental
impact associated with use of the product, and so forth), and so
forth.
Examples of subjective characterizing information include but are
not limited to user sensory perception information (regarding, for
example, heaviness or lightness, speed of use, effort associated
with use, smell, and so forth), aesthetics information (regarding,
for example, how attractive or unattractive the product is in
appearance, how well the product matches or accords with a
particular design paradigm or theme, and so forth), trustworthiness
information (regarding, for example, user perceptions regarding how
likely the product is perceived to accomplish a particular purpose
or to avoid causing a particular collateral harm), trendiness
information, and so forth.
This information 1004 can be curated (or not), filtered, sorted,
weighted (in accordance with a relative degree of trust, for
example, accorded to a particular source of particular
information), and otherwise categorized and utilized as desired. As
one simple example in these regards, for some products it may be
desirable to only use relatively fresh information (i.e.,
information not older than some specific cut-off date) while for
other products it may be acceptable (or even desirable) to use, in
lieu of fresh information or in combination therewith, relatively
older information. As another simple example, it may be useful to
use only information from one particular geographic region to
characterize a particular product and to therefore not use
information from other geographic regions.
At block 1003 the control circuit uses the foregoing information
1004 to form product characterization vectors for each of the
plurality of different products. By one approach these product
characterization vectors have a magnitude (for the length of the
vector and/or the angle of the vector) that represents a reduction
of exerted effort associated with the corresponding product to
pursue a corresponding user partiality (as is otherwise discussed
herein).
It is possible that a conflict will become evident as between
various ones of the aforementioned items of information 1004. In
particular, the available characterizations for a given product may
not all be the same or otherwise in accord with one another. In
some cases it may be appropriate to literally or effectively
calculate and use an average to accommodate such a conflict. In
other cases it may be useful to use one or more other predetermined
conflict resolution rules 1005 to automatically resolve such
conflicts when forming the aforementioned product characterization
vectors.
These teachings will accommodate any of a variety of rules in these
regards. By one approach, for example, the rule can be based upon
the age of the information (where, for example the older (or newer,
if desired) data is preferred or weighted more heavily than the
newer (or older, if desired) data. By another approach, the rule
can be based upon a number of user reviews upon which the
user-based product characterization information is based (where,
for example, the rule specifies that whichever user-based product
characterization information is based upon a larger number of user
reviews will prevail in the event of a conflict). By another
approach, the rule can be based upon information regarding
historical accuracy of information from a particular information
source (where, for example, the rule specifies that information
from a source with a better historical record of accuracy shall
prevail over information from a source with a poorer historical
record of accuracy in the event of a conflict).
By yet another approach, the rule can be based upon social media.
For example, social media-posted reviews may be used as a
tie-breaker in the event of a conflict between other more-favored
sources. By another approach, the rule can be based upon a trending
analysis. And by yet another approach the rule can be based upon
the relative strength of brand awareness for the product at issue
(where, for example, the rule specifies resolving a conflict in
favor of a more favorable characterization when dealing with a
product from a strong brand that evidences considerable consumer
goodwill and trust).
It will be understood that the foregoing examples are intended to
serve an illustrative purpose and are not offered as an exhaustive
listing in these regards. It will also be understood that any two
or more of the foregoing rules can be used in combination with one
another to resolve the aforementioned conflicts.
By one approach the aforementioned product characterization vectors
are formed to serve as a universal characterization of a given
product. By another approach, however, the aforementioned
information 1004 can be used to form product characterization
vectors for a same characterization factor for a same product to
thereby correspond to different usage circumstances of that same
product. Those different usage circumstances might comprise, for
example, different geographic regions of usage, different levels of
user expertise (where, for example, a skilled, professional user
might have different needs and expectations for the product than a
casual, lay user), different levels of expected use, and so forth.
In particular, the different vectorized results for a same
characterization factor for a same product may have differing
magnitudes from one another to correspond to different amounts of
reduction of the exerted effort associated with that product under
the different usage circumstances.
As noted above, the magnitude corresponding to a particular
partiality vector for a particular person can be expressed by the
angle of that partiality vector. FIG. 11 provides an illustrative
example in these regards. In this example the partiality vector
1101 has an angle M 1102 (and where the range of available positive
magnitudes range from a minimal magnitude represented by 0.degree.
(as denoted by reference numeral 1103) to a maximum magnitude
represented by 90.degree. (as denoted by reference numeral 1104)).
Accordingly, the person to whom this partiality vector 1001
pertains has a relatively strong (but not absolute) belief in an
amount of good that comes from an order associated with that
partiality.
FIG. 12, in turn, presents that partiality vector 1101 in context
with the product characterization vectors 1201 and 1203 for a first
product and a second product, respectively. In this example the
product characterization vector 1201 for the first product has an
angle Y 1202 that is greater than the angle M 1102 for the
aforementioned partiality vector 1101 by a relatively small amount
while the product characterization vector 1203 for the second
product has an angle X 1204 that is considerably smaller than the
angle M 1102 for the partiality vector 1101.
Since, in this example, the angles of the various vectors represent
the magnitude of the person's specified partiality or the extent to
which the product aligns with that partiality, respectively, vector
dot product calculations can serve to help identify which product
best aligns with this partiality. Such an approach can be
particularly useful when the lengths of the vectors are allowed to
vary as a function of one or more parameters of interest. As those
skilled in the art will understand, a vector dot product is an
algebraic operation that takes two equal-length sequences of
numbers (in this case, coordinate vectors) and returns a single
number.
This operation can be defined either algebraically or
geometrically. Algebraically, it is the sum of the products of the
corresponding entries of the two sequences of numbers.
Geometrically, it is the product of the Euclidean magnitudes of the
two vectors and the cosine of the angle between them. The result is
a scalar rather than a vector. As regards the present illustrative
example, the resultant scaler value for the vector dot product of
the product 1 vector 1201 with the partiality vector 1101 will be
larger than the resultant scaler value for the vector dot product
of the product 2 vector 1203 with the partiality vector 1101.
Accordingly, when using vector angles to impart this magnitude
information, the vector dot product operation provides a simple and
convenient way to determine proximity between a particular
partiality and the performance/properties of a particular product
to thereby greatly facilitate identifying a best product amongst a
plurality of candidate products.
By way of further illustration, consider an example where a
particular consumer as a strong partiality for organic produce and
is financially able to afford to pay to observe that partiality. A
dot product result for that person with respect to a product
characterization vector(s) for organic apples that represent a cost
of $10 on a weekly basis (i.e., CvP1v) might equal (1,1), hence
yielding a scalar result of .parallel.1.parallel. (where Cv refers
to the corresponding partiality vector for this person and P1v
represents the corresponding product characterization vector for
these organic apples). Conversely, a dot product result for this
same person with respect to a product characterization vector(s)
for non-organic apples that represent a cost of $5 on a weekly
basis (i.e., CvP2v) might instead equal (1,0), hence yielding a
scalar result of .parallel.1/2.parallel.. Accordingly, although the
organic apples cost more than the non-organic apples, the dot
product result for the organic apples exceeds the dot product
result for the non-organic apples and therefore identifies the more
expensive organic apples as being the best choice for this
person.
To continue with the foregoing example, consider now what happens
when this person subsequently experiences some financial misfortune
(for example, they lose their job and have not yet found substitute
employment). Such an event can present the "force" necessary to
alter the previously-established "inertia" of this person's
steady-state partialities; in particular, these negatively-changed
financial circumstances (in this example) alter this person's
budget sensitivities (though not, of course their partiality for
organic produce as compared to non-organic produce). The scalar
result of the dot product for the $5/week non-organic apples may
remain the same (i.e., in this example, .parallel.1/2.parallel.),
but the dot product for the $10/week organic apples may now drop
(for example, to .parallel.1/2.parallel. as well). Dropping the
quantity of organic apples purchased, however, to reflect the
tightened financial circumstances for this person may yield a
better dot product result. For example, purchasing only $5 (per
week) of organic apples may produce a dot product result of
.parallel.1.parallel.. The best result for this person, then, under
these circumstances, is a lesser quantity of organic apples rather
than a larger quantity of non-organic apples.
In a typical application setting, it is possible that this person's
loss of employment is not, in fact, known to the system. Instead,
however, this person's change of behavior (i.e., reducing the
quantity of the organic apples that are purchased each week) might
well be tracked and processed to adjust one or more partialities
(either through an addition or deletion of one or more partialities
and/or by adjusting the corresponding partiality magnitude) to
thereby yield this new result as a preferred result.
The foregoing simple examples clearly illustrate that vector dot
product approaches can be a simple yet powerful way to quickly
eliminate some product options while simultaneously quickly
highlighting one or more product options as being especially
suitable for a given person.
Such vector dot product calculations and results, in turn, help
illustrate another point as well. As noted above, sine waves can
serve as a potentially useful way to characterize and view
partiality information for both people and products/services. In
those regards, it is worth noting that a vector dot product result
can be a positive, zero, or even negative value. That, in turn,
suggests representing a particular solution as a normalization of
the dot product value relative to the maximum possible value of the
dot product. Approached this way, the maximum amplitude of a
particular sine wave will typically represent a best solution.
Taking this approach further, by one approach the frequency (or, if
desired, phase) of the sine wave solution can provide an indication
of the sensitivity of the person to product choices (for example, a
higher frequency can indicate a relatively highly reactive
sensitivity while a lower frequency can indicate the opposite). A
highly sensitive person is likely to be less receptive to solutions
that are less than fully optimum and hence can help to narrow the
field of candidate products while, conversely, a less sensitive
person is likely to be more receptive to solutions that are less
than fully optimum and can help to expand the field of candidate
products.
FIG. 13 presents an illustrative apparatus 1300 for conducting,
containing, and utilizing the foregoing content and capabilities.
In this particular example, the enabling apparatus 1300 includes a
control circuit 1301. Being a "circuit," the control circuit 1301
therefore comprises structure that includes at least one (and
typically many) electrically-conductive paths (such as paths
comprised of a conductive metal such as copper or silver) that
convey electricity in an ordered manner, which path(s) will also
typically include corresponding electrical components (both passive
(such as resistors and capacitors) and active (such as any of a
variety of semiconductor-based devices) as appropriate) to permit
the circuit to effect the control aspect of these teachings.
Such a control circuit 1301 can comprise a fixed-purpose hard-wired
hardware platform (including but not limited to an
application-specific integrated circuit (ASIC) (which is an
integrated circuit that is customized by design for a particular
use, rather than intended for general-purpose use), a
field-programmable gate array (FPGA), and the like) or can comprise
a partially or wholly-programmable hardware platform (including but
not limited to microcontrollers, microprocessors, and the like).
These architectural options for such structures are well known and
understood in the art and require no further description here. This
control circuit 1301 is configured (for example, by using
corresponding programming as will be well understood by those
skilled in the art) to carry out one or more of the steps, actions,
and/or functions described herein.
By one optional approach the control circuit 1301 operably couples
to a memory 1302. This memory 1302 may be integral to the control
circuit 1301 or can be physically discrete (in whole or in part)
from the control circuit 1301 as desired. This memory 1302 can also
be local with respect to the control circuit 1301 (where, for
example, both share a common circuit board, chassis, power supply,
and/or housing) or can be partially or wholly remote with respect
to the control circuit 1301 (where, for example, the memory 1302 is
physically located in another facility, metropolitan area, or even
country as compared to the control circuit 1301).
This memory 1302 can serve, for example, to non-transitorily store
the computer instructions that, when executed by the control
circuit 1301, cause the control circuit 1301 to behave as described
herein. (As used herein, this reference to "non-transitorily" will
be understood to refer to a non-ephemeral state for the stored
contents (and hence excludes when the stored contents merely
constitute signals or waves) rather than volatility of the storage
media itself and hence includes both non-volatile memory (such as
read-only memory (ROM) as well as volatile memory (such as an
erasable programmable read-only memory (EPROM).)
Either stored in this memory 1302 or, as illustrated, in a separate
memory 1303 are the vectorized characterizations 1304 for each of a
plurality of products 1305 (represented here by a first product
through an Nth product where "N" is an integer greater than "1").
In addition, and again either stored in this memory 1302 or, as
illustrated, in a separate memory 1306 are the vectorized
characterizations 1307 for each of a plurality of individual
persons 1308 (represented here by a first person through a Zth
person wherein "Z" is also an integer greater than "1"). It will be
appreciated that the number of persons and products for whom such
information is stored can be large. Storing partiality-based
information in a vectorized format can greatly ease both digital
storage requirements and computational resource requirements. Those
skilled in the art will appreciate these improvements to the
technical capabilities of both the memory and computer capabilities
of such a platform.
In this example the control circuit 1301 also operably couples to a
network interface 1309. So configured the control circuit 1301 can
communicate with other elements (both within the apparatus 1300 and
external thereto) via the network interface 1309. Network
interfaces, including both wireless and non-wireless platforms, are
well understood in the art and require no particular elaboration
here. This network interface 1309 can compatibly communicate via
whatever network or networks 1310 may be appropriate to suit the
particular needs of a given application setting. Both communication
networks and network interfaces are well understood areas of prior
art endeavor and therefore no further elaboration will be provided
here in those regards for the sake of brevity.
By one approach, and referring now to FIG. 14, the control circuit
1301 is configured to use the aforementioned partiality vectors
1307 and the vectorized product characterizations 1304 to define a
plurality of solutions that collectively form a multidimensional
surface (per block 1401). FIG. 15 provides an illustrative example
in these regards. FIG. 15 represents an N-dimensional space 1500
and where the aforementioned information for a particular customer
yielded a multi-dimensional surface denoted by reference numeral
1501. (The relevant value space is an N-dimensional space where the
belief in the value of a particular ordering of one's life only
acts on value propositions in that space as a function of a
least-effort functional relationship.)
Generally speaking, this surface 1501 represents all possible
solutions based upon the foregoing information. Accordingly, in a
typical application setting this surface 1501 will
contain/represent a plurality of discrete solutions. That said, and
also in a typical application setting, not all of those solutions
will be similarly preferable. Instead, one or more of those
solutions may be particularly useful/appropriate at a given time,
in a given place, for a given customer.
With continued reference to FIGS. 14 and 15, at optional block 1402
the control circuit 1301 can be configured to use information for
the customer 1403 (other than the aforementioned partiality vectors
1307) to constrain a selection area 1502 on the multi-dimensional
surface 1501 from which at least one product can be selected for
this particular customer. By one approach, for example, the
constraints can be selected such that the resultant selection area
1502 represents the best 95th percentile of the solution space.
Other target sizes for the selection area 1502 are of course
possible and may be useful in a given application setting.
The aforementioned other information 1403 can comprise any of a
variety of information types. By one approach, for example, this
other information comprises objective information. (As used herein,
"objective information" will be understood to constitute
information that is not influenced by personal feelings or opinions
and hence constitutes unbiased, neutral facts.)
One particularly useful category of objective information comprises
objective information regarding the customer. Examples in these
regards include, but are not limited to, location information
regarding a past, present, or planned/scheduled future location of
the customer, budget information for the customer or regarding
which the customer must strive to adhere (such that, by way of
example, a particular product/solution area may align extremely
well with the customer's partialities but is well beyond that which
the customer can afford and hence can be reasonably excluded from
the selection area 1502), age information for the customer, and
gender information for the customer. Another example in these
regards is information comprising objective logistical information
regarding providing particular products to the customer. Examples
in these regards include but are not limited to current or
predicted product availability, shipping limitations (such as
restrictions or other conditions that pertain to shipping a
particular product to this particular customer at a particular
location), and other applicable legal limitations (pertaining, for
example, to the legality of a customer possessing or using a
particular product at a particular location).
At block 1404 the control circuit 1301 can then identify at least
one product to present to the customer by selecting that product
from the multi-dimensional surface 1501. In the example of FIG. 15,
where constraints have been used to define a reduced selection area
1502, the control circuit 1301 is constrained to select that
product from within that selection area 1502. For example, and in
accordance with the description provided herein, the control
circuit 1301 can select that product via solution vector 1503 by
identifying a particular product that requires a minimal
expenditure of customer effort while also remaining compliant with
one or more of the applied objective constraints based, for
example, upon objective information regarding the customer and/or
objective logistical information regarding providing particular
products to the customer.
So configured, and as a simple example, the control circuit 1301
may respond per these teachings to learning that the customer is
planning a party that will include seven other invited individuals.
The control circuit 1301 may therefore be looking to identify one
or more particular beverages to present to the customer for
consideration in those regards. The aforementioned partiality
vectors 1307 and vectorized product characterizations 1304 can
serve to define a corresponding multi-dimensional surface 1501 that
identifies various beverages that might be suitable to consider in
these regards.
Objective information regarding the customer and/or the other
invited persons, however, might indicate that all or most of the
participants are not of legal drinking age. In that case, that
objective information may be utilized to constrain the available
selection area 1502 to beverages that contain no alcohol. As
another example in these regards, the control circuit 1301 may have
objective information that the party is to be held in a state park
that prohibits alcohol and may therefore similarly constrain the
available selection area 1502 to beverages that contain no
alcohol.
As described above, the aforementioned control circuit 1301 can
utilize information including a plurality of partiality vectors for
a particular customer along with vectorized product
characterizations for each of a plurality of products to identify
at least one product to present to a customer. By one approach
1600, and referring to FIG. 16, the control circuit 1301 can be
configured as (or to use) a state engine to identify such a product
(as indicated at block 1601). As used herein, the expression "state
engine" will be understood to refer to a finite-state machine, also
sometimes known as a finite-state automaton or simply as a state
machine.
Generally speaking, a state engine is a basic approach to designing
both computer programs and sequential logic circuits. A state
engine has only a finite number of states and can only be in one
state at a time. A state engine can change from one state to
another when initiated by a triggering event or condition often
referred to as a transition. Accordingly, a particular state engine
is defined by a list of its states, its initial state, and the
triggering condition for each transition.
It will be appreciated that the apparatus 1300 described above can
be viewed as a literal physical architecture or, if desired, as a
logical construct. For example, these teachings can be enabled and
operated in a highly centralized manner (as might be suggested when
viewing that apparatus 1300 as a physical construct) or,
conversely, can be enabled and operated in a highly decentralized
manner. FIG. 17 provides an example as regards the latter.
In this illustrative example a central cloud server 1701, a
supplier control circuit 1702, and the aforementioned Internet of
Things 1703 communicate via the aforementioned network 1310.
The central cloud server 1701 can receive, store, and/or provide
various kinds of global data (including, for example, general
demographic information regarding people and places, profile
information for individuals, product descriptions and reviews, and
so forth), various kinds of archival data (including, for example,
historical information regarding the aforementioned demographic and
profile information and/or product descriptions and reviews), and
partiality vector templates as described herein that can serve as
starting point general characterizations for particular individuals
as regards their partialities. Such information may constitute a
public resource and/or a privately-curated and accessed resource as
desired. (It will also be understood that there may be more than
one such central cloud server 1701 that store identical,
overlapping, or wholly distinct content.)
The supplier control circuit 1702 can comprise a resource that is
owned and/or operated on behalf of the suppliers of one or more
products (including but not limited to manufacturers, wholesalers,
retailers, and even resellers of previously-owned products). This
resource can receive, process and/or analyze, store, and/or provide
various kinds of information. Examples include but are not limited
to product data such as marketing and packaging content (including
textual materials, still images, and audio-video content),
operators and installers manuals, recall information, professional
and non-professional reviews, and so forth.
Another example comprises vectorized product characterizations as
described herein. More particularly, the stored and/or available
information can include both prior vectorized product
characterizations (denoted in FIG. 17 by the expression "vectorized
product characterizations V1.0") for a given product as well as
subsequent, updated vectorized product characterizations (denoted
in FIG. 17 by the expression "vectorized product characterizations
V2.0") for the same product. Such modifications may have been made
by the supplier control circuit 1702 itself or may have been made
in conjunction with or wholly by an external resource as
desired.
The Internet of Things 1703 can comprise any of a variety of
devices and components that may include local sensors that can
provide information regarding a corresponding user's circumstances,
behaviors, and reactions back to, for example, the aforementioned
central cloud server 1701 and the supplier control circuit 1702 to
facilitate the development of corresponding partiality vectors for
that corresponding user. Again, however, these teachings will also
support a decentralized approach. In many cases devices that are
fairly considered to be members of the Internet of Things 1703
constitute network edge elements (i.e., network elements deployed
at the edge of a network). In some case the network edge element is
configured to be personally carried by the person when operating in
a deployed state. Examples include but are not limited to so-called
smart phones, smart watches, fitness monitors that are worn on the
body, and so forth. In other cases, the network edge element may be
configured to not be personally carried by the person when
operating in a deployed state. This can occur when, for example,
the network edge element is too large and/or too heavy to be
reasonably carried by an ordinary average person. This can also
occur when, for example, the network edge element has operating
requirements ill-suited to the mobile environment that typifies the
average person.
For example, a so-called smart phone can itself include a suite of
partiality vectors for a corresponding user (i.e., a person that is
associated with the smart phone which itself serves as a network
edge element) and employ those partiality vectors to facilitate
vector-based ordering (either automated or to supplement the
ordering being undertaken by the user) as is otherwise described
herein. In that case, the smart phone can obtain corresponding
vectorized product characterizations from a remote resource such
as, for example, the aforementioned supplier control circuit 1702
and use that information in conjunction with local partiality
vector information to facilitate the vector-based ordering.
Also, if desired, the smart phone in this example can itself modify
and update partiality vectors for the corresponding user. To
illustrate this idea in FIG. 17, this device can utilize, for
example, information gained at least in part from local sensors to
update a locally-stored partiality vector (represented in FIG. 17
by the expression "partiality vector V1.0") to obtain an updated
locally-stored partiality vector (represented in FIG. 17 by the
expression "partiality vector V2.0"). Using this approach, a user's
partiality vectors can be locally stored and utilized. Such an
approach may better comport with a particular user's privacy
concerns.
It will be understood that the smart phone employed in the
immediate example is intended to serve in an illustrative capacity
and is not intended to suggest any particular limitations in these
regards. In fact, any of a wide variety of Internet of Things
devices/components could be readily configured in the same regards.
As one simple example in these regards, a computationally-capable
networked refrigerator could be configured to order appropriate
perishable items for a corresponding user as a function of that
user's partialities.
Presuming a decentralized approach, these teachings will
accommodate any of a variety of other remote resources 1704. These
remote resources 1704 can, in turn, provide static or dynamic
information and/or interaction opportunities or analytical
capabilities that can be called upon by any of the above-described
network elements. Examples include but are not limited to voice
recognition, pattern and image recognition, facial recognition,
statistical analysis, computational resources, encryption and
decryption services, fraud and misrepresentation detection and
prevention services, digital currency support, and so forth.
As already suggested above, these approaches provide powerful ways
for identifying products and/or services that a given person, or a
given group of persons, may likely wish to buy to the exclusion of
other options. When the magnitude and direction of the
relevant/required meta-force vector that comes from the perceived
effort to impose order is known, these teachings will facilitate,
for example, engineering a product or service containing potential
energy in the precise ordering direction to provide a total
reduction of effort. Since people generally take the path of least
effort (consistent with their partialities) they will typically
accept such a solution.
As one simple illustrative example, a person who exhibits a
partiality for food products that emphasize health, natural
ingredients, and a concern to minimize sugars and fats may be
presumed to have a similar partiality for pet foods because such
partialities may be based on a value system that extends beyond
themselves to other living creatures within their sphere of
concern. If other data is available to indicate that this person in
fact has, for example, two pet dogs, these partialities can be used
to identify dog food products having well-aligned vectors in these
same regards. This person could then be solicited to purchase such
dog food products using any of a variety of solicitation approaches
(including but not limited to general informational advertisements,
discount coupons or rebate offers, sales calls, free samples, and
so forth).
As another simple example, the approaches described herein can be
used to filter out products/services that are not likely to accord
well with a given person's partiality vectors. In particular,
rather than emphasizing one particular product over another, a
given person can be presented with a group of products that are
available to purchase where all of the vectors for the presented
products align to at least some predetermined degree of
alignment/accord and where products that do not meet this criterion
are simply not presented.
And as yet another simple example, a particular person may have a
strong partiality towards both cleanliness and orderliness. The
strength of this partiality might be measured in part, for example,
by the physical effort they exert by consistently and promptly
cleaning their kitchen following meal preparation activities. If
this person were looking for lawn care services, their partiality
vector(s) in these regards could be used to identify lawn care
services who make representations and/or who have a trustworthy
reputation or record for doing a good job of cleaning up the debris
that results when mowing a lawn. This person, in turn, will likely
appreciate the reduced effort on their part required to locate such
a service that can meaningfully contribute to their desired
order.
These teachings can be leveraged in any number of other useful
ways. As one example in these regards, various sensors and other
inputs can serve to provide automatic updates regarding the events
of a given person's day. By one approach, at least some of this
information can serve to help inform the development of the
aforementioned partiality vectors for such a person. At the same
time, such information can help to build a view of a normal day for
this particular person. That baseline information can then help
detect when this person's day is going experientially awry (i.e.,
when their desired "order" is off track). Upon detecting such
circumstances these teachings will accommodate employing the
partiality and product vectors for such a person to help make
suggestions (for example, for particular products or services) to
help correct the day's order and/or to even effect
automatically-engaged actions to correct the person's experienced
order.
When this person's partiality (or relevant partialities) are based
upon a particular aspiration, restoring (or otherwise contributing
to) order to their situation could include, for example,
identifying the order that would be needed for this person to
achieve that aspiration. Upon detecting, (for example, based upon
purchases, social media, or other relevant inputs) that this person
is aspirating to be a gourmet chef, these teachings can provide for
plotting a solution that would begin providing/offering additional
products/services that would help this person move along a path of
increasing how they order their lives towards being a gourmet
chef.
By one approach, these teachings will accommodate presenting the
consumer with choices that correspond to solutions that are
intended and serve to test the true conviction of the consumer as
to a particular aspiration. The reaction of the consumer to such
test solutions can then further inform the system as to the
confidence level that this consumer holds a particular aspiration
with some genuine conviction. In particular, and as one example,
that confidence can in turn influence the degree and/or direction
of the consumer value vector(s) in the direction of that confirmed
aspiration.
All the above approaches are informed by the constraints the value
space places on individuals so that they follow the path of least
perceived effort to order their lives to accord with their values
which results in partialities. People generally order their lives
consistently unless and until their belief system is acted upon by
the force of a new trusted value proposition. The present teachings
are uniquely able to identify, quantify, and leverage the many
aspects that collectively inform and define such belief
systems.
A person's preferences can emerge from a perception that a product
or service removes effort to order their lives according to their
values. The present teachings acknowledge and even leverage that it
is possible to have a preference for a product or service that a
person has never heard of before in that, as soon as the person
perceives how it will make their lives easier they will prefer it.
Most predictive analytics that use preferences are trying to
predict a decision the customer is likely to make. The present
teachings are directed to calculating a reduced effort solution
that can/will inherently and innately be something to which the
person is partial.
Pursuant to various embodiments, systems, apparatuses and methods
are provided herein useful to provide a more convenient shopping
experience by pre-filling a customer's shopping cart according to
the customer partialities in a user profile that are compared or
matched with products having a similar or compatible identified
vectorized product characterizations. These suggested retail items
may be those which a consumer or shopper was already likely to
purchase, items they might like to purchase given the customer's
inclinations, or it may include retail items on which the customer
might be interested in receiving additional information. To
expedite or facilitate the improved shopping experience, the
shopping carts may be loaded or prepared when a customer is within
a particular store's vicinity or when a customer is detected within
a certain distance from the store. Alternatively, the shopping
carts may be prepared in a particular requested time frame at a
particular requested location.
By pre-filling a shopping cart, a customer or shopper is able to
reduce the amount of time spent shopping in a physical retail
facility, which are sometimes quite large and/or sprawling. This
can be particularly beneficial if the suggested items loaded into
the customer's cart would be out of the way for the customer. In
addition, by providing suggested retail items that are loaded into
a customer's physical shopping cart, the customer may avoid
shopping in certain areas of the store that are inconvenient or
maybe problematic for the customer or those accompanying the
customer. For example, parents with children may be able to have
products pre-loaded into their carts that allow them to avoid
visiting areas near or around a toy department of a retail shopping
facility.
In some embodiments, a shopping system directed to pre-filling
shopping carts with retail items prior to a customer's arrival at
the physical retail shopping facility includes a user database of
user profiles having one or more partialities associated with the
customers or users therein, a product database of retail products
with identified vectorized product characterizations, a plurality
of physical shopping carts, and a control circuit. By one approach,
the control circuit is configured to access the user database and
the product database and identify one or more suggested retail
items for a particular customer based, in part, on comparisons
between the identified partialities for that customer in their user
profile and the identified vectorized product characterizations of
the retail products. Further, in one embodiment, one of the
plurality of the shopping carts designated for a particular
customer is filled with one or more suggested retail items prior to
the particular customer's arrival at the physical retail
facility.
By one approach, the system includes a staging area with numerous
shopping carts designated for use by particular customers and
having suggested retail items for the particular customers loaded
therein. In one illustrative example, the staging area is a
location near the entrance or exit, wither inside or outside, of
the physical retail facility such that the shoppers or customers
may easily retrieve their designated shopping carts just before
they begin shopping within the facility.
In one illustrative embodiment, the physical retail facility has a
rejection bin where customers may deposit unwanted suggested retail
items. By one approach, the rejection bin includes a bin sensor
configured to detect placement of the unwanted suggested retail
items therein. Furthermore, in one exemplary embodiment, the
control circuit is configured to update the one or more
partialities for a given customer in the user database, in part, on
the particular customer placing one of the unwanted suggested
retail items into the rejection bin.
As used herein, the shopping carts may include baskets with wheels,
flatbed carts, bins, and handheld baskets, among others that are
moved around a retail facility or location. Further, the retail
facility may be any type of shopping facility or location in which
products are displayed, for sale, and/or distributed at numerous
points around the facility such that the customer travels through
the space to retrieve desired products. Further, the facility may
be any of a number of sizes or formats and may include products
from one or more merchants. For example, a facility may be a single
store operated by one merchant or may be a collection of stores
covering multiple merchants such as a mall.
In one illustrative approach, the system includes one or more point
of sale terminals in communication with the control circuit such
that the system can update the user profile and the user's
partialities associated with the particular customer based, in
part, on retail products purchased by the particular customer at
the point of sale terminal and the vectorized product
characterizations associated with the purchased retail
products.
As suggested above, the shopping carts may be loaded or prepared
when a customer is within a particular store's vicinity or when a
customer is detected within a certain distance from the store. In
this manner, the control circuit may receive location information
from the particular customer indicating that the customer is
approaching the retail facility, such as by indicating that the
customer is within a certain distance from the facility or that the
customer is in the parking lot of the facility. By another
approach, a customer's electronic user device may notify the
facility when the device's navigation system has set the facility
as a destination.
In another embodiment, the shopping carts may be prepared by a
particular time and at a particular requested location. In this
manner, the control circuit is configured to receive, from the
particular customer, a collection time and a collection location
for picking up the customer's designated shopping cart with at
least one suggested retail item.
In one approach, the user profiles in the database include a
purchase history. Further, in one approach, the purchased retail
products have vectorized product characterization associated
therewith that impact the partialities associated with the user in
the user profile. Accordingly, in one approach, the control circuit
is configured to identify the one or more suggested retail items
based, in part, on the purchase history of a particular
customer.
As used herein the shopping system may be implemented at a number
of retail shopping facilities and the control circuit may be
configured to update the user database according to purchases at
multiple retail facilities. Further, in addition to purchases made
at physical retail facilities, the shopping system may update the
user database in light of purchases made online, via a mobile
application, or over the phone, among other shopping methods.
In one exemplary embodiment, the partialities may be represented by
partiality vectors and can include values, preferences, and
affinities. Further, the shopping system may identify customer or
user partialities using data obtained from other sources outside of
a customer's purchase history. For example, partialities may be
identified based on calendar appointments, charitable donations,
shopping habits, age, and profession, among many others, a few of
which are outlined below. Accordingly, the user database may be
updated according to the partialities identified outside of the
purchase history.
Given the information in the user database and the product
database, the control circuit, in one approach, is configured to
analyze the partiality vectors and the vectorized product
characterizations and identify overlap therebetween. The suggested
retail products may be determined based on this overlap. In this
manner, the suggested retail products for a given customer may
change based on changes in the user and product databases. For
example, a change to the product database, such as the addition of
a particular new product, may result in the particular new product
being a suggested retail item for a particular customer, even
though this suggested retail item was not previously purchased by
the particular customer.
In operation, the method may include maintaining a database of user
profiles with partialities and a product database with identified
vectorized product characterizations or product vectors associated
therewith. By one approach, the method includes identifying one or
more suggested retail items for a particular customer based, in
part on comparisons between the identified partialities of the user
profile and the identified vectorized product characterizations of
the retail products and loading some or all of these suggested
retail products into a designated shopping cart before the customer
arrives at the physical retail shopping facility.
FIG. 18 illustrates a simplified block diagram of an exemplary
shopping system 1800, according to some embodiments, which provides
for pre-filling shopping carts 1805 according to a customer's
partialities that can be represented by partiality vectors
characterizing a customer's values, preferences, and affinities.
The shopping system 1800 includes a user profile database 1810
(which may be similar to the memory 1306 of the vectorized
characterizations 1307 of individual persons 1308), a retail
product database 1812 with retail products having identified
vectorized product characterizations or product vectors (which may
be similar to the library 911 or the memory 1303 with vectorized
characterizations 1304 for products 1305), and a central computer
or a central computer or control circuit 1806 (which may be similar
to other control circuits discussed herein). The control circuit
1806 is configured to access the databases 1810, 1812 to identify
one or more suggested retail items for a particular customer based,
in part, on comparisons with the partialities of a particular
customer and the identified vectorized product characterizations of
the retail products. Further, one of the shopping carts 1805 at a
physical retail facility can be filled with one or more of the
suggested retail items prior to the customer arriving at the
shopping facility.
In one approach, the user profiles in the user profile database
1810 include a purchase history. Further, one or more of the
purchased retail products have at least one identified partiality
associated therewith. In one approach, the control circuit is
configured to identify the suggested retail items based, in part,
on the purchase history of a particular customer by having the
partialities identified with the purchased items associated with
the customer. The user profiles in the database also may have
partialities associated therewith based on other information, such
as, for example, the rejection of a suggested item or demographic
information, among many others. Other partialities may be
identified by receiving specific requests from a customer, such as
receiving a notification from a customer that they do not wish to
have produce pre-loaded into their cart. By one approach, these
request are received from the customer when the customer retrieves
the pre-loaded cart or when the customer checks out at a point of
sale terminal.
In one illustrative example, a customer may desire not to take a
route through the store that brings the customer within the
vicinity of the toy department, and this could be indicated in
their user profile in the database 1810 such that the suggested
retail items may include previously purchased items and other
suggested retail items in the vicinity of the toy department. By
one approach, indications in the user profile may be specifically
provided by the customer or may be inferred based customer actions,
such as the time or day of the week in which the customer typically
shops.
As used herein the shopping system may be implemented at a number
of retail shopping facilities 1814 and the control circuit 1806 may
be configured to update the user profile database 1810 according to
purchases at multiple retail facilities. As noted above, the
partialities may be represented by partiality vectors and can
include values, preferences, and affinities. Further, as suggested
above, the shopping system 1800 may identify user partialities
using data obtained from other sources outside of a customer's
purchase history. For example, partialities may be identified based
on calendar appointments, charitable donations, age, and
profession, among many others. Accordingly, the user database may
be updated according to the partialities identified outside of the
purchase history.
Given the information in the user database and the product
database, the control circuit, in one approach, is configured to
analyze the partiality vectors and the vectorized product
characterizations and identify overlap therebetween. The suggested
retail products may be determined based on this overlap. In this
manner, the suggested retail products for a given customer may
change based on changes in the user and product databases. For
example, a change to the product database or the user database, may
result in the different suggested retail items for a particular
customer.
By one approach, the system 1800 includes a staging area 1816 with
numerous shopping carts 1805 designated for use by particular
customers and having suggested retail items for the particular
customers loaded therein. In one illustrative example, the staging
area is near the entrance or exit, either inside or outside, of the
physical retail facility 1814. The staging area 1816 may be the
location where the shopping carts 1805 are pre-filled and the area
where the shopping carts are held until being retrieved by
particular customers.
In one illustrative embodiment, the physical retail facility has a
rejection bin 1818 where customers may deposit unwanted suggested
retail items. By one approach, the rejection bin 1818 includes a
bin sensor configured to detect placement of the unwanted suggested
retail items therein. Furthermore, the control circuit may be
configured to update the one or more partialities in the user
profile database 1810 based, in part, on the particular customer
placing one of the unwanted suggested retail items into the
rejection bin and the bin sensor detecting the placement and
notifying the control circuit 1806 accordingly.
In one illustrative approach, the system 1800 includes one or more
point of sale terminals 1802 in communication with the control
circuit 1806 such that the system 1800 can update the user profile
associated with the particular customer based, in part, on retail
products purchased by the particular customer at the point of sale
terminal 1802 and the vectorized product characterizations
associated with those purchased retail products.
In one illustrative example, the shopping carts 1805 are loaded or
prepared when a customer is within a particular store's vicinity or
when a customer is detected within a certain distance from the
store. In this manner, the control circuit 1806 may receive
location information from the particular customer indicating that
the customer is approaching the retail facility, such as by
indicating that the customer is within a certain distance from the
facility or that the customer is in the parking lot of the
facility. In one illustrative example, a customer's electronic user
device 1804 may notify the facility when the device's navigation
system has set the facility as a destination. In yet another
illustrative example, a customer or user may notify the control
circuit 1806 of arrival information by using an application or
mobile feature of an electronic user device 1804. In this manner,
the shopping cart 1805 may be prepared for a user by a particular
time and at a particular requested location. To that end, the
control circuit 1806 is configured to receive, from the particular
customer via the electronic user device 1804, a requested
collection time and a collection location that the customer wishes
to retrieve the shopping cart with suggested retail items.
Alternatively, the control circuit 1806 may analyze the information
in the user profile database 1810 to estimate when and where a
customer is likely to shop at a particular physical retail facility
1814 and may instruct the staging area 1816 to prepare or load a
shopping cart 1805 with suggested retail items according to the
overlap between the customer's vectorized partialities and the
identified vectorized characterizations of the retail products.
As illustrated in FIG. 18, the control circuit 1806, the electronic
user device 1804, the user profile database 1810, the retail
product database 1812, the shopping cart staging area 1816, the
item rejection bin 1818, and the point of sale terminal 1802, if
present, may be communicatively coupled, either directly or
indirectly, such as over one or more distributed communication
networks 1808, which may include, for example, LAN, WAN, Internet,
cellular, Wi-Fi, and other such communication networks or
combinations of two or more of such networks.
Referring now to FIG. 19, a process 1900 for pre-filling shopping
carts 1805 within a physical retail facility 1814 according to some
embodiments discussed herein is illustrated. The method 1900
includes, for example, maintaining 1902 a user profile database
with one or more identified partialities associated with particular
customers. As noted above, the partialities may be represented by
partiality vectors such that a customer's values, preferences, and
affinities are captured in the particular user profile in the
database. Also, the user profile may include numerous partiality
vectors for a particular customer.
The method 1900 also includes maintaining 1904 a retail product
database with retail products having vectorized product
characterizations. In one illustrative approach, a particular
retail product in the retail product database 1904 may have
numerous vectorized product characterizations associated
therewith.
By one approach, the method 1900 includes identifying 1906 one or
more suggested retail items for a particular customer based, in
part, upon comparisons between the identified partialities in the
user profile and the identified vectorized product
characterizations of the retail products. Furthermore, in step
1908, the method includes loading some or all of these suggested
retail products into a designated shopping cart 1805 before the
customer arrives at the physical retail shopping facility.
In step 1910, the method may include providing the designated
shopping carts at a staging area within the physical retail
facility 1814. By one approach, the staging area 1816 is disposed
near an entrance or exit of the retail facility 1814.
In step 1912, the method may include updating a user profile based,
in part, on the associated customer rejecting a suggested retail
item that was pre-loaded into the designated shopping cart. In this
manner, the partialities in the user profile are updated to
indicate that the customer did not wish to purchase a suggested
item.
In step 1914, the method may include updating the user profile
based, in part, on purchase information received about a particular
customer from a point of sale terminal 1802 at which the customer
purchased retail items. By one approach, the control circuit may
receive such purchase information from other stores or online
shopping services and update the user profile accordingly.
In step 1916, the method may include receiving from a particular
customer a collection time and a collection location for picking up
a designated shopping cart filled with suggested retail items. As
noted above, this can be accomplished a number of ways, such as,
for example, through a user's electronic device.
In step 1918, the method may include receiving location information
from a customer and loading suggested retail items into a
designated shopping cart as the customer approaches the retail
facility, which can be determined in a number of manners as
discussed above.
Pursuant to various embodiments, systems, apparatuses and methods
are described herein that enhance customers' retail shopping
experiences at a retail shopping facility. The system comprises a
retail environment control system with one or more control circuits
that identify customers that are present at a retail shopping
facility, and access a customer profile database that maintains
customer profiles for each of multiple different customers. Each
customer profile includes at least a set of customer partiality
vectors corresponding to the customer and are directed quantities
that each have both a magnitude and a direction, with the direction
representing a determined order imposed upon material space-time by
a particular partiality and the magnitude represents a determined
magnitude of a strength of the belief, by the corresponding
customer, in a benefit that comes from that imposed order. The
system further identifies a set of recommended products each having
at least a threshold relationship between corresponding product
partiality vectors and one or more of a set of partiality vectors
associated with a customer. A recommendation listing of the set of
recommended products can be generated and communicated to cause the
recommendation listing to be presented to the corresponding
customer while that customer is still physically at the shopping
facility.
Some embodiments utilize the partiality vectors in improving a
customer shopping experience. This can be extended to shopping
within a retail shopping facility to on-line shopping and beyond.
Further, some embodiments utilize the partiality vectors to
identify products that are likely to be of interest and/or
purchased by a customer and provide information to the customer
while the customer is physically at a shopping facility to enhance
the customer's shopping experience.
FIG. 20 illustrates a simplified block diagram of a retail system
2000, in accordance with some embodiments. The retail system 2000
includes a retail environment control system 2002 that is
communicatively coupled with one or more inventory systems 2004 and
one or more databases 2006 over one or more distributed computer
and/or communication networks 1310. The databases 2006 can include
one or more product databases storing at least product partiality
vectors 1304, and one or more customer databases that include
customer partiality vectors 1307. The system 2000 further includes
one or more display systems 2008 and audio systems 2010 that are
located at the retail shopping facility and typically strategically
placed in various locations throughout at least the sales floor of
the retail facility. The retail environment control system further
communicatively couples with one or more sensor systems 2014 that
can detect movement, recognize customers, track customer movement,
and the like. For example, the sensor systems may include camera,
motion sensors, radio frequency identifier (RFID) sensors, wireless
signal sensors (e.g., Wi-Fi access points), other such sensor
systems, and typically a combination of two or more of such sensor
systems. In some embodiments, the retail environment control system
is further configured to wirelessly communicate with customers'
user interface units 2012 (e.g., smart phones, tablets, and other
such mobile devices).
The retail system 2000 typically include one or more purchasing
systems 2016 that enable a retail entity to receive payment from a
customer purchasing one or more products through the retail system
2000. In some embodiments, the system includes a customer profile
system 2018 that receives, generates, maintains and updates
customer information associated with multiple different customers,
including customer partiality vector information. The customer
profiles generated and/or maintained by the customer profile system
may be stored in memory 1306 as part of a customer database and/or
customer profile database. The system may include a product profile
system 2020 that receives, generates, maintains and updates product
information and/or profiles associated with multiple different
products, including product partiality vector information. The
product information generated and/or maintained by the product
profile system 2020 may, in some implementations, be stored in
memory 1303 as part of a product database and/or product profile
database. In some embodiments, the inventory system 2004 provides
some of the product information to be used by the product profile
system in assembling and maintaining product profiles and/or
partiality vector information.
The retail environment control system 2002, at least in part,
controls information provided to customers while the customers are
shopping at a retail shopping facility to help the customer
identify products of interest and/or products a customer is likely
to purchase. Further, in some embodiments, the retail environment
control system uses sensor information received from one or more
sensors 2014 to determine, in part, when and what product and/or
marketing information to present to a particular customer. This
information can be presented through the display and/or audio
systems 2008, 2010, and/or the customers' user interface units. The
customers' user interface units may wirelessly communicate with the
retail environment control system through cellular, Wi-Fi,
Bluetooth, other such wireless communication methods, or
combination of two or more of such methods.
The retail environment control system in part uses customer
partiality vectors and their association with one or more product
partiality vectors of products to identify products that the
customer is likely to purchase, and from those products identify a
set of one or more products that system is to recommend or market
to a particular customer as the customer travels through the retail
shopping facility. Further, the retail environment control system
can identify and/or track a customer's location within the shopping
facility to identify when a customer is near, within a threshold
distance of and/or approaching one or more of the products intended
to be recommended to the customer through the display and/or audio
systems and/or the customer's user interface unit.
The retail environment control system in part controls aspects of a
shopping facility and/or communicates with customers' user
interface units 2012 to enhance customers' shopping experiences. In
some embodiments, the retail environment control system identifies
customers that are present at a retail shopping facility. The
identification can be through one or more methods such as facial
and/or body image recognition, detecting a communication from a
customer's previously registered user interface unit and/or the
customer's user interface unit logging into a local area network at
the shopping facility (e.g., a Wi-Fi, network, Bluetooth network,
etc.), the customer identifying her/himself (e.g., by
scanning/swiping a retailer supplied customer card,
scanning/swiping a credit card, entering a user name and/or
password at a station or kiosk, other such methods, or combination
of such methods), RFID detection of a customer assigned RFID tag
(e.g., key ring, card, etc.), finger print analysis, voice
analysis, other such methods or combination of two or more of such
methods.
Using sensor information from the one or more sensors 2014, the
retail environment control system can further detect and/or track
customers' movements through the shopping facility. Again, the
sensor information can include facial and/or body recognition,
tracking movement of a customer's user interface unit 2012 (e.g.,
by tracking Wi-Fi access points with which the user interface unit
communicates, receiving GPS coordinates, RFID detection and/or
transmission from the user interface unit, other such methods of
combination of such methods), RFID signals (e.g., specific to a
customer (e.g., customer's keychain, a card specific to a customer,
etc.), on a shopping cart that the retail environment control
system as identified is associated with one or more specific
customers, etc.), other such methods, or combination of two or more
of such methods.
The retail environment control system further includes and/or
accesses mapping information of the retail shopping facility that
includes specific location and/or placement information of products
within the shopping facility. In some embodiments, this product
location information may be maintained by the inventory system
2004. The system further has specific location and/or placement
information of multiple display systems and/or audio systems (or
parts of such display systems or audio systems that can be
separately controlled). For example, in some embodiments, the
system 2000 includes multiple display devices (e.g., televisions,
computers, computer monitors, etc.) that are positioned and at
various locations distributed throughout at least some portions of
the shopping facility. The display devices may be of substantially
any size and positioned at substantially any location (including
potentially any height). As a further example, relatively small
display devices maybe portable and temporarily fixed directly on a
shelf in front of products placed on that shelf, other display
devices mounted within the floor of an isle with a protective
surface over the top allowing customers to walk over and/or push
shopping carts over the display, mounted from the ceiling, mounted
at the end of isles, and/or placed in other locations. In some
instances, the display devices include audio devices as part of the
audio system(s). Additionally or alternatively, separate speakers
or other such audio output and/or input devices can be positioned
at various locations throughout the shopping facility. The precise
location of each display device and audio device (whether part of
or separate from a display device) can be known and mapped in the
mapping of the shopping facility. Additionally or alternatively,
some embodiments include display and/or audio systems attached with
shopping carts, riding-scooters, or other devices that customers
use while moving through the shopping facility. Location
information can be communicated from these portable display and/or
audio devices to the retail environment control system (e.g., based
on GPS, detected locations from encoded light information, scanning
of location codes (e.g., location bar codes and/or RFID signals),
other such location information, or combination of such
information).
Similarly, some embodiments receiving location information and/or
track a relative location of customers' user interface units. As
described above, the system can receive GPS data from the user
interface units, detect access to a wireless network access point,
other such methods, or combination of such methods. The retail
environment control system can use the customers' locations and/or
determined direction of travel, and the location information of
products to identify products that are relevant to the customer's
location and/or a location the customer is approaching and that the
customer is more likely to be interested in and purchase.
In some embodiments, the retail environment control system accesses
customer partiality vectors. For example, the retail environment
control system may access a customer profile database that
maintains a customer profile for each of multiple different
customers. The customer profiles may include a set of customer
partiality vectors corresponding to the customer. Again, the
customer partiality vectors are directed quantities that each have
both a magnitude and a direction, with the direction representing a
determined order imposed upon material space-time by a particular
partiality and the magnitude represents a determined magnitude of a
strength of the belief, by the corresponding customer, in a benefit
that comes from that imposed order. In some embodiments, the use of
the partiality vectors attempts to minimize stress in customers'
lives. Often stress is a function of time that is inefficiently
used and/or wasted, wasted physical effort, wasted mental thought,
doing things that are in opposition with a customer's partialities,
etc. These elements of stress include objective data (e.g.,
purchase history, weather, standard time needed to prepare a meal,
etc.) and subjective data (e.g., looking at social media and other
public behavior to determine a consumer profile and/or partiality
vectors. By identifying products that have threshold alignment with
customers' partiality vectors, the system can identify solutions
that may minimize customers' stress, and satisfy the needs of the
customers.
The retail environment control system can evaluate product
partiality vectors relative to a customer's one or more partiality
vectors. A set of one or more recommended products can be
identified that each have at least a threshold relationship between
corresponding product partiality vectors and one or more of
customer partiality vectors associated with the customer. The
relationships between customers' partiality vectors and product
partiality vectors can be based on the directional aspect, the
magnitude aspect, or some combination of the directional and
magnitude aspects. In some instances for example, the correlation
should satisfy a threshold directional correlation or alignment and
a magnitude threshold. The thresholds may vary between customers
and/or products. Further, the thresholds may be sliding thresholds
such that when a greater correlation between one of the directional
aspect or the magnitude aspect is identified, a lower threshold for
the other of the magnitude aspect or directional aspect has to be
met to consider the product to be sufficiently consistent with that
customer and/or the customer's partiality vectors. Again, those
products and/or services that align with a customer's partiality
vector are typically those products and/or services that are
determined to be perceived by a customer as provide a significant
benefit or the most benefit to that customer.
Based the identification of products that correspond with the
customer's partiality vectors the system can identify one or more
products that correspond to a customer's current location or a
location that the customer is approaching. For example, the system
may identify a set of one or more recommended products that each
have at least one product partiality vector that has a threshold
relationship with one or more of the customer's partiality vectors,
and are further within a threshold distance of the customer's
current location and/or is located at a location within the
shopping facility where the customer is predicted to be within a
threshold period of time based on a previous route travelled by the
customer and a rate of travel through the shopping facility. In
some embodiments, the retail environment control system generates a
recommendation listing of a set of one or more recommended
products, which may include and/or define accesses to
recommendation content corresponding to one or more products of the
recommendation listing. The recommendation content may include
information about a product, marketing information, and other such
information.
In some embodiments, the retail environment control system causes
the recommendation listing and/or other relevant information
corresponding to the set of recommended products and causes the
recommendation listing to be presented to the corresponding
customer while the customer is still physically at the shopping
facility. This can include causing the recommendation listing or a
portion of the recommendation listing to be communicated to one or
more display systems 2008 and/or audio systems 2010 that are within
a rendering threshold distance of the customer. For example, the
retail environment control system may detect that a customer is
entering an isle that includes laundry detergents, and cause a
display system at the beginning of the isle to display a
recommendation listing and/or other relevant product information
regarding an identified laundry detergent that has one or more
partiality vectors that are within a threshold alignment of one or
more of the customer's partiality vectors.
Additionally or alternatively, in some embodiments, the retail
environment control system may wirelessly communicate the
recommendation listing or part of the recommendation listing of the
set of recommended products to a mobile user interface unit
associated with the customer and causes content representative of
the recommendation listing to be displayed on the user interface
unit. The recommended information and/or listing may be displayed
and/or audibly played back through a customer's user interface
unit, by accessing a web site, by implementing application software
(APP) that receives content, information, instructions and/or code
to cause the rendering of the recommendation information, or other
such software implemented on a user interface unit. Accordingly,
the retail environment control system can cause web pages to be
generated and distributed to customers' user interface units, cause
content, information, instructions and/or code to be communicated
to user interface units, other such communications, or a
combination of two or more of such communications. The
recommendation content can include the code, graphics information,
organization information and the like to be present the
recommendation information corresponding to products that have
partiality vectors that have at least a threshold alignment with
one or more of the customer's partiality vectors.
Again, the information presented to the customer typically
corresponds in part to the customer's location within the shopping
facility while the recommendation information (e.g., recommendation
listing, marketing information, product information, highlighted
information, etc.) is displayed and/or audibly played back to the
customer. Some embodiments establish one or more boundaries and/or
geo-fences relative to and/or about display and/or audio systems
and/or strategic locations throughout the shopping facility. The
boundaries can in part correspond to one or more threshold
distances from a display and/or audio system, a strategic location,
or the like. Again, in some embodiments, a series of multiple
display systems (and/or audio systems) are each positioned at
different locations throughout the shopping facility. The retail
environment control circuit can be configured to obtain the
location of a customer within the shopping facility. In some
instances, sensor information may be received from one or more
sensors and/or from other relevant sources that indicate a
customer's location within the shopping facility. The system can
communicate the recommendation content and/or listing to a display
system of the multiple display systems that is within a threshold
distance of the obtained location of the customer. The threshold
distance relative to a particular display system may vary depending
on a direction the customer is traveling. For example, a first
threshold distance may be associated with a display system when the
customer traveling a first direction with the customer facing the
display device, while a second threshold may be defined for the
same display device when the customer is traveling in an opposite
direction (e.g., the customer cannot see the display system when
traveling in the opposite direction until very close to the display
system).
Further, the system can include a series of multiple audio output
systems 2010 that can be positioned at different locations
throughout the shopping facility. The retail environment control
system can obtain a location of the customer within the shopping
facility communicate the recommendation listing to an audio output
system of the multiple audio output systems that is within a
threshold distance of the obtained location of the customer. The
threshold distance corresponding to an audio system may, in some
embodiments, be significantly less than threshold distance for
displayed content as the audio may be distracting to other
customers.
The recommendation content and/or listing can identify one or more
products (e.g., a textually and/or audibly identify, and/or display
and/or audibly playback corresponding trademark information, etc.),
and display and/or audibly present characteristics of a product,
display and/or audibly present marketing information, other such
information, or combination of two or more of such information. In
some embodiments, the retail environment control system identifies,
from product information of a recommended product of the set of
recommended products, at least a product partiality vector that has
the threshold relationship with at least one of the customer's
partiality vectors, and causes marketing information,
representative of at least the identified product partiality vector
associated with the recommended product, to be displayed as part of
the recommendation listing being presented to the customer.
Further, the presentation of recommended product information is
typically presented based on a customer's location within the
shopping facility. A location of the customer within the shopping
facility can be obtained and/or determined. This location
information can be determined based on information from one or more
sensor (e.g., image recognition based on image and/or video
processing of images or video from a known camera directed at a
known location within the shopping facility), RFID information from
one or more RFID scanners, distance sensors, other such sensor
information, and often a combination of sensor information from two
or more sensors. Further, the location information may be based on
a customer scanning a particular product using their user interface
unit or a mobile scanning device may available by the shopping
facility. Based in part on the scan, the system can identify the
product and estimate a customer's location based on a known
location of the scanned product within the shopping facility. In
some instances, the system additionally uses other location
information in combination with the scanned information to
determine a location (e.g., previously identified that a customer
was in a first location, and based on that first location determine
that the customer is in a second location based on the scan and a
relationship between the first location and the determined location
of the scanned product).
The recommendation information and/or listing may, in some
instances, be limited to emphasize products the retail environment
control system anticipates the customer is more likely to purchase
and/or for which the correlation between the product partiality
vector and the customer's partiality vectors have a threshold
correlation. Similarly, the environment control system may, in
identifying the set of recommended products, identify each of the
recommended products of the set of recommended products that each
correspond to at least one product previously selected by the
customer during the customer's current visit to the shopping
facility. Further, the set of recommended products is typically
identified based on the set of partiality vectors and on the
location of the customer within the shopping facility.
In some embodiments, the retail environment control system further
uses customer actions and/or feedback from the customer to
determine a level of agreement between the recommended products,
the products purchased, the correlation between product partiality
vectors and customer's partiality vectors, and the accuracy of the
customer's partiality vectors. The system may receive a response
from the customer indicating a level of agreement of the
recommendation corresponding to at least one recommended product of
the set of recommended products. This feedback may be based on a
customer purchasing the recommended product, the customer stopping
to consider the recommended product relative to one or more
threshold durations, the customer passing the recommended product
without considering the product, the customer communicating a
notice (e.g., a text message), a customer responding to an inquiry,
other such feedback, or a combination of two or more of such
feedbacks. In some embodiments, the retail environment control
circuit can adjust one or more customer and/or product partiality
vectors of a set of partiality vectors based on the level of
agreement. In some embodiments, the retail environment control
system can track a customer's movements relative to recommendation
information provided to the customer (e.g., determine what products
a customer looks at and/or considers), to determine whether to
adjust partiality vectors, thresholds of alignment between product
and customer partiality vectors, and the like. For example, the
system can track a customer's behavior relative to recommendation
information presented to the customer within the store to adjust
partiality vectors.
Some embodiments cause marketing information to be displayed that
corresponds to one or more products, and often correspond to
products that have a threshold relationship with the customer's
partiality vectors. One or more products may be identified, based
on and/or from product information for one or more recommended
products, that have at least one product partiality vector that has
the threshold relationship with at least one of the first
customer's partiality vectors. Marketing information consistent
with product partiality vector(s) that has/have the relationship
with the customer's partiality vector(s) can be identified and
accessed. In some implementations, the marketing information may be
included in and/or referenced in a product profile, identified in a
database and associated with that customer and/or that partiality
vector, or the like. Instructions, content and/or information can
be communicated to a display and/or audio system, and/or a
customer's user interface unit to cause the marketing information
representative of at least a corresponding product partiality
vector to be displayed in association with the recommendation
information and/or listing.
Further, information that is more likely to be considered important
to the customer and/or may more likely influence the customer in
making a purchasing decision can be provided to the customer to
help the customer identify products that are consistent with the
customer's partiality vectors. For example, information about a
product being developed without animal testing may be displayed in
relation to a product when a customer has a strong partiality
vector corresponding to animal rights and/or opposition to animal
cruelty. The display of the information can simplify shopping for
the customer because the customer is more easily able to identify
products that customer likely wants to purchase. Further, the
information may provide the customer the feeling that there is an
emotional relationship with the retailer because the customer may
feel that the retailer understands the customer and appreciates the
customer's partialities and/or values. Some embodiments may further
provide customized incentives to the customer based on an alignment
of the incentive with the customer's partiality vectors and/or a
prediction that a particular product, which has been identified to
have product partiality vectors that align within one or more
thresholds of the customer's partiality vectors, in attempt to get
the customer to make a purchase. Similarly, the system may
incentive the customer to share product information with one or
more other potential customers.
The system, in some embodiments, can use subsequent purchases,
products viewed and/or considered by the customer, customers'
movements through the shopping facility in relation to
recommendation information displayed or otherwise played back to
the customers, products disregarded and/or ignored by the customer,
and other such actions made by the customer to evaluate the impact
and/or likelihood that the displayed product information had on the
customer's purchases, and when relevant make adjustments to the
future display of product information. Further, the actions taken
by the customer, the product information, the product organization,
and the like can be used as a feedback to the system to
additionally or alternatively make adjustments, including
adjustments to the directional aspect and/or magnitude aspect of
one or more of the customer's partiality vectors and/or product
partiality vectors.
In some embodiments, graphics, information, instructions and/or
code are communicated to a user interface unit to cause the
marketing information to be displayed as an emphasized
characteristic of the one or more recommended products. For
example, the marketing information may be highlighted, temporarily
displayed over a graphically rendered image of the products, other
such methods or combination of two or more of such methods.
Some embodiments prioritize products relative to at least the
customer's partiality vectors. Some embodiments determine product
prioritization, at least in part, as a function of a level of
correlation between the product partiality vectors of the multiple
recommended products and a customer's set of partiality vectors.
For example, in some embodiments, define greater priority to
products that have a greater correlation with the customer's
partiality vectors. Further, the priority is typically different
for each customer for which partiality vectors are established. As
such, different customers have different prioritized products, and
product prioritization information defining a priority, a level of
priority, a prioritization class, other such prioritization, or
combination of two or more of such prioritization, can be generated
based on the correlation between products' partiality vectors and a
customer's partiality vectors.
Product prioritization information may further prioritize multiple
recommended products of a set of recommended products. Some or all
of the product prioritization information can be used in generating
the recommendation information. The prioritization may further take
into consideration customer's historic purchases, preferences and
other aspects. Some embodiments identify, based on customer
information (e.g., from the customer profile database, which may be
part of the customer database 1306), product preferences
corresponding to one or more of the multiple recommended products.
The product preferences may, at least in part, correspond to a
customer's historic tendency of purchasing one or more products
over one or more secondary similar products that the customer has
purchased. In some implementations, the product prioritization
information can be determined, at least in part, as a function of
the product preferences and the level of correlation between the
product partiality vectors of multiple recommended products and the
customer's set of partiality vectors.
Some embodiments further utilize customer's previous historic
purchases and/or a purchase history in customizing the
recommendation information and/or listing. For example, a
customer's historic purchases can be used to identify customization
parameters specific to the customer and correlated to purchased
products. The customization parameters can be substantially any
relevant parameter that defines aspects or features of products
tended to be purchased by a customer. For example, the
customization parameters may include one or more sizes of products
(e.g., size(s) of pants that are purchased, size(s) of shoes that
are purchased), colors that a customer tends to purchase,
quantities the customer tends to purchase when products are
available in different quantities (e.g., small quantities versus
family pack size), and other such customization parameters. The
customization parameters may be determined as an average, based on
a percentage of purchases, recent changes in purchase patterns,
other such aspects or combinations of such aspects. Further, some
embodiments apply weightings to some purchases in determining a
customization. For example, more recent purchases may be given
greater weight than older purchases, consistent purchases may be
given greater weight over individual or small number purchases, and
other such weightings.
The retail environment control system can use the customization
parameters to determine and/or modify recommendation information
and/or listings corresponding to one or more of the set of
recommended products and/or other products to be consistent with
customization parameters and with available actual products. In
some embodiments, the retail environment control system
communicates with the inventory system 2004 to determine whether a
recommended product (or other product) is available with
characteristics that are consistent with the customization
parameters. For example, the system may identify a pair of shoes
that the system wants to recommend, and can access the inventory
system and/or an inventory database to determine whether the
recommended shoes are available in the size the customer typically
purchases and/or in what colors are available. When the product is
not available in a particular size the retail environment control
system may not include the product as a recommended product or may
cause a notification to be displayed in association with the
displayed recommended product that the customer would have to wait
for shipment.
In some embodiments, the retail environment control system 2002
accesses the databases 2006 to obtain the customer partiality
vectors and product partiality vectors. The databases may be
maintained by one or more retail entities, or maintained by a third
party and accessible to the retail environment control system. The
databases, in some applications, optimize the data storage and/or
association between various partiality vectors. The databases may
include one or more tables that increase flexibility, provide
faster search times, and smaller memory requirements. For example,
some embodiments the databases comprise series of interdependent
cells, with a first set of cells associated with or defining a
specific customer with a second set of customer partialities
vectors that associate specific customers to relevant partiality
vectors with magnitudes for each of the relevant partiality vectors
specified within the cell associated between the specific customer
and the relevant partiality vectors. In some embodiments, the same
database further includes a set of cells associated with or
defining a specific product that similarly cross reference the
partiality vectors with magnitudes for each of the relevant
partiality vectors specified within the cell associated between the
specific product and the relevant partiality vectors. As such,
cells are interdepending while reducing storage space, and speeding
access to the relationships between customers, products and
partiality vectors. In other embodiments, a separate product
database is maintained with the cells defining the association
between the specific products and the product partiality vectors.
The system in evaluating products relative to customers can
implement an optimized correlation analysis between the customer
partiality database and the product partiality database to identify
one of a correlation between one or more defined customer
partiality vectors for a customer, and product partiality vectors
for one or more products, and to further identify a threshold
correlation between one of the directional aspect and the magnitude
aspect of the customer's partiality vectors and the correlated one
or more products. Further, in some applications, the database
organization reduces memory by having customer partialities and
product partialities reference the same direction and magnitude
cells when they are the same.
The product partiality vectors may be provided and/or defined by a
product manufacturer, distributor, supplier or other third party
service. Further, in some applications, the retail environment
control system 2000 may learn over time product partiality vectors.
For example, the system may identify over time customers that
purchase a particular product. Based on a commonality of one or
more customer partiality vectors between customers purchasing the
particular product, the retail environment control system may
associate one or more product partiality vectors consistent with
one or more of the common one or more customer partiality vectors.
In some implementations, the retail environment control system may
further receive input from one or more customers and/or workers
identifying that a product should be associated with a particular
product partiality vector. The retail environment control system
may further consider the customer or worker submitting the request,
and provide different levels of authority to different customers
and workers to identify potential product partiality vectors.
FIG. 21 illustrates a simplified flow diagram of a process 2100 of
enhancing customers' retail shopping experiences, in accordance
with some embodiments. In step 2102, one or more customer that are
present at a retail shopping facility are identified. In step 2104,
a customer profile database is accessed. In some embodiments, the
customer profile database maintains a customer profile for each of
multiple different customers, and each customer profile comprises a
set of customer partiality vectors corresponding to the
customer.
In step 2106, a set of one or more recommended products are
identified that each have at least a threshold relationship between
corresponding one or more product partiality vectors and one or
more of a set of partiality vectors associated with the one or more
customers. In step 2108, a recommendation listing of the set of
recommended products is communicated causing the recommendation
listing to be presented to the customer while the customer is still
in the shopping facility. Some embodiments receive a response from
the customer indicating a level of agreement of the recommendation
corresponding to at least one recommended product of the set of
recommended products. One or more partiality vector of the set of
partiality vectors may, in some instances, be adjusted based on the
level of agreement.
In some embodiments, the recommendation information, which may
comprise a recommendation listing of the set of recommended
products, is wirelessly communicated to a mobile user interface
unit associated with an intended customer, and causes content
representative of the recommendation information and/or listing to
be displayed on the user interface unit. Some embodiments obtain a
location of a customer within the shopping facility, and the
recommendation listing can be communicated to one or more display
systems that are within one or more threshold distances of the
location of the customer. The system may include a series of
multiple display systems that are each positioned at different
locations throughout the shopping facility. Similarly, some
embodiments communicate some or all of the recommendation
information and/or listing to one or more audio output systems that
are within one or more threshold distances of the location of the
customer. In some embodiments, the system may include a series of
multiple audio output systems each positioned at different
locations throughout the shopping facility.
Some embodiments identify, from product information of one or more
recommended product of the set of recommended products, at least
one product partiality vector that has the threshold relationship
with at least one of the customer's partiality vectors. The system
can cause marketing information, representative of at least the
product partiality vector associated with the recommended product,
to be displayed as part of the recommendation information and/or
listing being presented to the customer. In some embodiments, the
customer's location can further be considered in identifying
relevant products. The location of the customer within the shopping
facility can be obtained, and one or more products of the set of
recommended products may be identified based on the set of
partiality vectors and on the location of the customer within the
shopping facility. In some embodiments, the system in identifying
the set of recommended products may identify each of the
recommended products of the set of recommended products that each
correspond to at least one product previously selected by the
customer during the customer's current visit to the shopping
facility.
In some embodiments, systems, apparatus and corresponding methods,
comprise: a retail environment control circuit coupled with memory
storing instructions that when executed by the control circuit
cause the control circuit to: identify that a first customer is
present at a retail shopping facility; access a customer profile
database, wherein the customer profile database maintains a
customer profile for each of multiple different customers, and each
customer profile comprises a set of customer partiality vectors
corresponding to the customer, wherein the customer partiality
vectors are directed quantities that each have both a magnitude and
a direction, with the direction representing a determined order
imposed upon material space-time by a particular partiality and the
magnitude represents a determined magnitude of a strength of the
belief, by the corresponding customer, in a benefit that comes from
that imposed order; identify a first set of recommended products
each having at least a threshold relationship between corresponding
product partiality vectors and one or more of a set of partiality
vectors associated with the first customer; and communicate a
recommendation listing of the first set of recommended products and
causing at least a portion of the recommendation listing to be
presented to the first customer while the first customer is still
physically at the shopping facility.
Some embodiments comprise methods of enhancing customers' retail
shopping experiences, comprising: by a retail environment control
circuit: identifying that a first customer is present at a retail
shopping facility; accessing a customer profile database, wherein
the customer profile database maintains a customer profile for each
of multiple different customers, and each customer profile
comprises a set of customer partiality vectors corresponding to the
customer, wherein the customer partiality vectors are directed
quantities that each have both a magnitude and a direction, with
the direction representing a determined order imposed upon material
space-time by a particular partiality and the magnitude represents
a determined magnitude of a strength of the belief, by the
corresponding customer, in a benefit that comes from that imposed
order; identifying a first set of recommended products each having
at least a threshold relationship between corresponding product
partiality vectors and one or more of a set of partiality vectors
associated with the first customer; and communicating a
recommendation listing of the first set of recommended products and
causing the recommendation listing to be presented to the first
customer while the first customer is still in the shopping
facility.
Pursuant to various embodiments, systems, apparatuses and methods
are provided herein useful to enhance customers' shopping
experiences. Some embodiments provide a retail product presentation
system associated with one or more retail shopping facilities where
customers enter to purchase different retail products. The system
includes a central control system that is communicatively coupled
with one or more product attribute databases associated with the
retail shopping facility. The product attribute database at least
in part associates products available for purchase through the
retail shopping facility with one or more product attributes that
each define an attribute of the product (e.g., type of product,
size of product, weight of product, quantity within a package,
flavor, flavors, color, colors, shape, dimensions, materials,
manufacturing and/or development attributes (e.g., where made,
claim of no child labor, claim of no animal testing, etc.), claims
of health and/or marketing (e.g., claim of not genetically
modified, claim of "organic", etc.), other products that can use
the product, other products that the product can use, other
products that the product can be used with, other products that are
associated with the product, etc.). The system can receive
requested product information from a customer regarding one or more
product attributes corresponding to a first type of product for
which the customer is shopping. A set of products can be identified
from the product attribute database that have product attributes
that correspond to the received product attributes. A set or
assortment of one or more different products can be selected from
the set of products in response to the request. Based on the
selected products, the system can cause at least one of each of the
assortment of different products to be physically collected and
positioned on at least one product support system of a plurality of
product support systems at the shopping facility each configured to
receive and support multiple products. The product support system
and at least the assortment of the different products can be
physically presented to the customer while at the shopping
facility.
FIG. 22 illustrates a simplified block diagram of an exemplary
retail system 2200 configured to physically present an assortment
of products to customers, in accordance with some embodiments. The
system includes one or more central control systems 2202 and
multiple product support systems 2204. The central control system
is communicatively coupled with one or more databases 2206 over a
computer and/or communication network 2210. The databases can store
and maintain substantially any relevant information for use by the
system such as but not limited to one or more product attribute
databases 2206, one or more partiality vector databases, one or
more customer databases, one or more inventory databases, other
such databases, and typically a combination of two or more of such
databases.
In some embodiments, the retail system 2200 includes multiple
customer or user interface systems 2214 that allow customers to
interact with the system, such as entering product information
and/or product attribute information of products that customers are
interested in purchasing. Multiple sensors and/or sensor systems
2216 are typically included in the system and positioned at
multiple different locations throughout a shopping facility (e.g.,
proximate entrances and exits, proximate customer interface systems
2214, proximate locations where product support systems are
presented to customers, on product support systems, proximate
product staging areas, distributed throughout a product storage
area, other such locations, and typically two or more of such
locations). One or more points of sale systems 2218 are further
included in some embodiments to complete sales of products
purchased by customers. Some embodiments include an inventory
system 2222 that tracks quantities and/or locations of inventory
throughout the shopping facility. The inventory system may, in some
applications, further assist and implement product orders from a
distribution center, fulfillment center, suppliers, manufacturers
and/or other sources.
In some embodiments, the system may include one or more product
routing systems 2224. The product routing system can direct the
retrieval and routing of products from storage locations, such as
in a back storage area of the shopping facility, to staging areas
where products are to be positioned on product support systems
2204, to locations on the sales floor, from a loading bay to a
storage location, from a loading bay to a location on the sales
floor, and/or other such product routing. Routing instructions may
be communicated to workers regarding a location where a product can
be retrieved, quantity of the product, and/or instructions on where
the product is to be routed. In some implementations, the product
routing system can include one or more conveyor systems that can
transport products, retrieval systems that can place products in
storage locations and retrieve products from storage locations,
forklift, drones and/or other such vehicles, and other such
systems. Further, the product routing system can direct workers
regarding the retrieval and placement of products in desired
locations. One or more support transport system 2226 are included
in some embodiments and are configured to transport the product
support systems 2204 to and from storage locations, staging areas
and product display locations. In some embodiments, some or all of
the databases 2206, the point of sale systems 2218, support
transport system 2226, product routing system 2224, inventory
system 2222, customer interface system 2214, and/or other systems
can be implemented through the central control system 2202.
Further, some embodiments enable communication with and/or include
user interface units 2228 of workers of the shopping facility
and/or customers. The user interface units can be one or more of a
variety of user interface units including, but not limited to,
mobile and/or handheld electronic devices such as so-called smart
phones and portable computers such as tablet/pad-styled computers,
laptops, computers, custom shopping facility units (e.g., scanners,
two-way communication devices, etc.), and other such devices.
The product support systems 2204 are each configured to receive and
physically support multiple products to be presented to the
customers. The product support systems may include one or more
shelves, racks, clamps, ramps and/or other such supports that can
receive and support products. In some embodiments, the product
support systems are configured to be transported between at least a
staging area where products are loaded onto the product support
systems and one or more viewing locations where a customer can be
positioned to view selected products that have been placed onto one
or more product support systems for consideration of purchase. In
some embodiments, the product support systems are configured to
cooperate with and be transported by the support transport system
2226, which may include one or more conveyor systems, drones,
forklift type devices, and/or other such methods of transport. For
example, the product support systems may include one or more
couplers to at least temporarily couple with an overhead conveyor
system that can move the product support system to an intended
location, which may be based on instructions from the product
routing system 2224, central control system 2202, customer
interface systems 2214, or other source.
Some embodiments access and/or maintain one or more product
attribute databases that associate products available for purchase
through one or more retail shopping facilities with one or more
product attributes that each define an attribute of the product. As
presented above, these attributes define characteristics, features,
marketing, claims and other such information about a product.
Further, the attribute information in part can be used to identify
products based on input from customers requesting products that
have certain attributes or attributes that relate to certain
attributes.
The central control system 2202 is configured to receive requested
product information from customers regarding one or more product
attributes corresponding to one or more types of products for which
a customer is shopping and/or may be interested in purchasing. Such
information may include, but is not limited to, categories of a
product (e.g., breakfast cereal, produce, ice cream, frozen pizza,
meat, drinks, etc.), sub-categories of products (e.g., fruit,
vegetables, carbonated drink, sports drink, chicken, ground beef,
ice cream bars, sugary breakfast cereal, high fiber breakfast
cereal, etc.), information about an intended consumer, information
about size, information about advertising claims, information about
claimed benefits of a product, and/or other such attribute
information. The product information and/or attribute information
may be received through one or more customer interface systems 2214
at a shopping facility, a customer's user interface unit 2228, or
other such source. In some embodiments, the system 2200 includes a
plurality of customer interface systems 2214 distributed throughout
the shopping facility and each configured to enable customers to
enter product attributes and/or characteristics about products the
customer is interested in purchasing and based on which assortments
are selected. One or more customer interface systems may be located
proximate locations where product support systems are presented to
customers, while one or more other customer interface systems may
be located away from an area where a product support system is
presented to customers. The customer interface systems can include
a user interface 216, which can include for example one or more
displays, touchscreens, inputs (e.g., buttons, trackball, mouse,
keyboard, etc.), and the like to allow the customer to view and
enter information, select information, and/or select options
presented to the customer.
The central control system can use the product attribute
information to cause a search through the attribute database. A set
of products can be identify from the product attribute database
that have product attributes that correspond to the received
product attributes and/or have at least one attribute of the
customer specified attributes. In some instances, other related
attributes can be identified based on the customer specified
attributes. For example, a customer specified attribute may be
closely related to one or more other attributes (e.g., a
sub-category specified by a customer is closely related to one or
more categories of which the sub-category is specified as being
part of). Accordingly, one or more related attributes may further
be identified and used in implementing a search through the product
attribute database. Some embodiments may further access customer
information corresponding to the customer submitting the request
and identify one or more other attributes that correspond to or may
relate to one or more attributes specified by the customer. The
other attributes may be identified based on a customer's purchase
history, previous product searches, previously specified product
attributes, partiality vector associations, preferences, other such
information, and often a combination of such information. Further,
the set of products may be products the customer has purchased in
the past, products the system identifies as products that the
customer would be interested in, and/or other such products.
In some embodiments, an assortment of different products are
selected from the set of products, which may include all of the set
of products or a sub-set of one or more products of the set of
products. The selection can be based on one or more factors such as
but not limited to customer partiality vectors, customer's purchase
history, customer's preferences, emphasis applied by a customer to
one or more attributes, order attributes are entered, number of
products within the set of products, number of products the system
predicts the customer would want to consider, expected size of the
product support system with which the products are to be
positioned, number of products of one or more other selected
assortments of yet other products that are to be supported by the
product support system, other such factors, or a combinations of
two or more of such factors. Typically, the different products of
an assortment each have a relationship to one or more of the
product attributes being considered, and often have a relationship
with each of multiple product attributes. For example, the product
attributes identified by the customer may specify: breakfast
cereal, whole grain, and with fruit. Accordingly, multiple
different products may be identified that each are a breakfast
cereals (or a breakfast cereal bar, or other such product when
relevant), that have a threshold quantity of whole grains and/or
advertise whole grain or grains as a feature, and further include
fruit (e.g., raisins, dried cranberries, dried apples, a paste of
from dates, etc.). From the set of multiple products that have a
threshold association with one or more of the product attributes,
the system can select one or more of these product to define an
assortment of products. Further, the system may identify multiple
different sizes of boxes of product of the assortment (e.g.,
cereal). Accordingly, the selection of the assortment may further
select product size, quantity, color, and/or other attributes,
which may correspond to attributes specified and/or may be
determined based on one or more factors, such as described above
and further below.
Some embodiments maintain and/or access one or more partiality
vector databases. These partiality databases can associates
different customer identifiers each specific to a different
customer and a set of customer partiality vectors corresponding to
a respective one of the customer identifiers. Customer partiality
vectors are directed quantities that each have both a magnitude and
a direction. The direction represents a determined order imposed
upon material space-time by a particular partiality. The magnitude
represents a determined magnitude of a strength of the belief, by
the corresponding customer, in a benefit that comes from that
imposed order. Similarly, the partiality databases can includes
product identifiers and corresponding product partiality vectors
that can define an order that may be affected by the product and a
magnitude of that affect. In selecting the assortment of different
products, the central control system may access the one or more
partiality vector databases and select the assortment of different
products that each have at least a threshold relationship between
corresponding product partiality vectors and one or more of the
customer's partiality vectors. Further, in some embodiments, the
central control system may consider inquiries from other customers
in selecting products of an assortment.
As introduced above, some embodiments maintain and/or access a
plurality of customer profiles each associated with a different
customer. The customer profiles can, in part, maintain purchase
history information. The central control system can use the
purchase history information in selecting assortments of different
products. Accordingly, the central control system can accesses a
customer profile associated with a customer that is to be shown
products, and can select an assortment of different products based
on the purchase history information of that customer in accordance
with the corresponding customer profile.
Some embodiments may further identify one or more products that are
not part of the set of products but is considered to be related to
and/or relevant to one or more products of a selected assortment of
products. For example, the customer may specify an attribute of a
"sleeping bag", and the system may use other information to
determine that the customer is interested in camping and present
other products related to camping onto the product support system
to be presented to the customer in addition to the products of the
assortment. Similarly, the central control system can identify
common attributes across multiple different types of products that
the customer is interested in. For example, the customer may have
requested "tents" and "lanterns", and based on these requests can
determine the customer may further be interested in bug spray.
Accordingly, the central control system may select an assortment of
bug stray and present the bug spray to the customer separate from
or in cooperation with the tents and/or lanterns.
Based on the selected assortment, the central control system can
cause at least one of each of the different products of the
selected assortment to be physically collected and positioned on
one or more product support systems. The number of products of each
of the different products can depend on the number of products the
customer indicated he/she is interested in purchasing, a predicted
number of the product the customer is expected to purchase (e.g.,
based on purchase history, cost, duration between purchases,
duration between visits to a shopping facility, etc.), size of the
product support systems, and other such factor. The selected
products are placed onto the product support system loading the
product support system with specific products that the customer is
interested in and/or predicted to be interested in viewing. When
space permits, different assortments of products can be loaded onto
a single product support system.
The central control system can further direct and cause the loaded
product support system with the different products of the selected
assortment of products to be physically presented to the customer
while the customer is at the shopping facility and allowing the
customer to physically interact with the products on the product
support system. In some embodiments, the products can be loaded by
workers and/or an automated system onto a product support system in
view of the customer. In other implementations, a loaded product
support system can be transported by the support transport system
2226 (e.g., a conveyor system, a worker, rotated, etc.) to a
location where the customer is waiting or other location to which
the customer is directed. For example, the customer may be standing
at a customer interface system 2214, and one or more product
support systems can be transported to a location behind and/or
adjacent the customer interface system. In some instances, for
instance, a staging area may be below a sales floor on which the
customer is interacting with the customer interface system, and the
one or more product support systems may be raised to the level of
the sales floor. Similarly, the product support system may simply
rotate to present the physical products to a customer. Further, the
product support systems are typically transported to a location
that is readily accessible to the customer so that the customer can
look, pickup and consider the multiple different products of the
assortment, as well as to easily reach and retrieve a desired
number a product selected by the customer. In some instances, the
customer may indicate through the customer interface system and/or
an interface on the product support system the desire for one or
more additional of a selected product when there is an insufficient
number of the customer selected product.
Typically, the system enables multiple different customers to be
simultaneously supported so that different assortments of products
can be presented different customers. As such, one or more other
assortments of different products can be physically collected for
another customer. The central control system can cause the one or
more other assortments of the different products to be physically
presented to the other customer while the first assortment is
physically presented to the first customer and at a physical
location within the shopping facility that is different than a
physical location where the first assortment is being presented to
the first customer.
In some embodiments, the customer may indicate through the customer
interface system, user interface unit 2228, and/or the system can
detect through one or more sensors that the customer has completed
consideration of the one or more assortments of products that were
positioned on the product support system. The central control
system can cause the product support system and/or the unselected
products to be returned to a staging area and/or a reallocation
area. In some instances, the central control system and/or the
product routing system in cooperation with the inventory system can
direct the products to be put into relevant storage locations.
These locations can be recorded by the inventory system to be used
in subsequent retrieval of products. In other embodiments,
depending on the one or more assortments of products, the products
may be left on the product support system, such as when the system
knows and/or anticipates one or more other customers being
interested in viewing the assortment of products. The anticipated
interest can be based on one or more other customers that are in
the shopping facility and/or expected to be visiting the shopping
facility, and the customer profile, partiality vectors, purchase
history, etc. of those one or more customers. Further, one or more
products on the product support system may be replaced with other
products based on a subsequent customer and/or attributes
identified by the subsequent customer.
In some embodiments, one or more sensors 2216 are positioned
relative to the product support system and configured to detect one
or more product of one or more assortments of the different
products selected from the product support system by the customer.
The sensors can include substantially any relevant sensor, such but
not limited to optical based scanning sensors to sense and read
optical patterns (e.g., bar codes), radio frequency identification
(RFID) tag reader sensors capable of reading RFID tags in proximity
to the sensor, motion sensors, scales, cameras, image processing
systems, other such sensors or combination of two or more of such
sensors. The sensor systems can communicate product identifier
information of the one or more products selected by the customer to
the central computer system. The product identification information
can be communicated to a point of sale system 2218 that can charge
the customer for the product and obtain payment from the customer
for at least the selected one or more products (e.g., based on a
credit card previously or subsequently provided by the customer,
based on a customer account, etc.). Further, some embodiments use
the selection in subsequent evaluations in selecting products of an
assortment.
As presented above, the customers can submit requests to view
products having one or more product attributes. The customers can
specify attributes through the customer interface systems 2214,
customers' user interface units 2228, and/or other such systems. In
some embodiments, customers can submit requests while remote from
the shopping facility. Based on the request, the system can
identify the set of one or more products corresponding to the
attributes and select the assortment of one or more products. The
assortment of products can be pre-staged prior to the customer
entering the shopping facility and/or while the customer shops in
one or more other areas of the shopping facility. In some
embodiments, the central control system can receive, from a
customer and prior to the customer entering the shopping facility,
a listing of multiple different types of products that the customer
is interested in purchasing. For each of the multiple different
types of products, a separate assortment of one or more different
products of that type of product can be selected. Each of the
different assortments of the different products are directed to be
physically collected. In some instances, when the customer submits
the request with sufficient time, the assortments of different
products can be collected prior to the customer entering the
shopping facility, and positioned on one or more product support
systems. These loaded product support systems can be stages in a
staging area or other area to await the customer. Further, the
system may enable the customer to schedule at time to visit the
shopping facility, and the central control system can coordinate
the staging of products for one or more customers based on the
appointments of different customers. Customers scheduled to arrive
at a later time and the product attributes submitted by these
scheduled customers may further be considered in selecting products
of an assortment for a first customer. For example, the system may
select a product of an assortment for a first customer because it
also corresponds with one or more attributes of a second
customer.
The central control system may identify when the customer has
entered and/or is in the shopping facility. This may be based on a
communication from the customer, detecting the presence of a mobile
user interface unit associated with the customer, detecting an RFID
chip associated with a customer (e.g., in a customer card carried
by the customer), the customer registering (e.g., through a
customer interface system), voice recognition, facial recognition,
other methods, or combination of two or more of such methods. In
some instances, the central control system may communicate with the
customer providing instructions on where the customer is to go
within the shopping facility where the loaded one or more product
support systems can be presented to the customer. Some embodiments
may have a limited number of product presentation locations where
loaded product support systems can be presented to customers.
Further, some presentation locations may be larger than others,
with smaller areas used to present a threshold number of products
to a customer, while other larger presentation locations may be
intended to present customers other threshold numbers of products.
For example a first set of presentation locations may be configured
to present less than 225 products, a second set of presentation
location may be configured to present less than 230 products, and a
third presentation location may be configured to present less than
2200 products. Further, while products are presented to a customer
at a presentation location, further products may be staged on
subsequent product support systems so that even when a customer
intends to view more products than the threshold, one or more
subsequent product support systems can be sequentially removed once
a customer has selected from that product support system and be
replaced with another loaded product support system, which can
allow a customer to be presented with substantially any number of
products. In some instances, for example, a display, customer
interface system, kiosk, notification to a customer's user
interface system and/or other such system can notify the customer
of a location where the customer can be presented the loaded
products. In other embodiments, the customer may proceed to an open
presentation location and the central control system can receive a
notification and/or detect that the customer is at the open
location (e.g., based on sensor data, the customer submitting a
notification through a customer interface system or user interface
unit, etc.).
The central control system can cause the loaded one or more product
support systems and the different assortments of the different
products can be physically presented to the customer. In some
instances, the central control system waits for the customer to be
at a location, while in other instances can issue instructions to
cause one or more loaded product support systems to be transported
to the location prior to the customer being at the location (e.g.,
as the customer walks to the location in the shopping facility). In
some embodiments, the product support systems are able to support
more than a single assortment of products and/or multiple product
support systems can be used to support multiple different
assortments of products. Accordingly, in some instances, the
central control system can cause multiple different assortments of
the different products to be simultaneously presented to the
customer. Further, the presented products are typically organized
according to the different assortments to allow the customer to
readily identify the different assortments.
By physically moving and presenting products to customer, the sales
floor space occupied by the presentation location can be used to
present any number of different products, and thus is not limited
to a single product or single type of product. This can allow the
shopping facility to carry a greater number of products than might
otherwise be presented because products can be more efficiently
stored in a storage area that does not have to be readily
accessible to customers and products do not have to be positioned
in the back storage at heights that can be reached by an adult
human standing on the floor. The movement of products to the
customer simplifies the customer's shopping experience, in part,
because the customer is provided with options for the products the
customer is interested in without having to navigate through the
shopping facility to various locations to obtain desired products.
Further, in some instances, the size of the sales floor can be
reduced because less space is needed while still allowing customers
to physically access the large number of different and varying
products.
The one or more product storage areas can store the large numbers
of products. The inventory system 2222 can be configured to track
the precise location of each product within the back storage area
and can provide location information to the product routing system
2224, which can direct the retrieval of the products from the
storage locations to a staging area or one of multiple staging
areas. Further, the inventory system can implement a strategic
retrieval of products, such as considering expiration dates to
select the oldest product to be presented, consider an expected
freshness of products, consider location of products, and the like.
In some embodiments, the product routing system can include one or
more conveyor systems positioned in the storage area. The conveyor
system(s) can include a series of conveyor roller systems, belt
systems, trolley systems, hook systems, and/or other such conveyor
systems that are configured to transport products between storage
locations (or proximate storage locations) and one of the staging
areas where products are placed onto the product support systems.
The conveyor system can include one or more routing devices (e.g.,
arms, guide rollers, etc.) that can be controlled to direct product
along various conveyors. Further, some embodiments include
retrieval devices, which may be part of the conveyor system and/or
separate from the conveyor system. The retrieval devices that can
be configured to move vertically and/or horizontally (e.g., along
and/or into storage racks) to retrieve products to be deposited
onto a conveyor and/or transported by the retrieval devices to the
staging area. Additionally or alternatively workers can be directed
to retrieve products from storage locations and placed onto the
conveyor system or otherwise routed to one of multiple staging
areas. In some embodiments, the central control system causes the
different products of an assortment to be physically collected by
issuing one or more instructions to the conveyor system, one or
more retrieval devices, one or more workers, etc. with a product
identifier and a storage location of each of the different products
of the assortment. The instructions can further instruct the
conveyor system, one or more retrieval devices, one or more
workers, etc. to retrieve each of the different products of the
assortment.
FIG. 23 illustrates a simplified flow diagram of an exemplary
process 2300 of providing a customized shopping experience for
customers and presenting retail products to customers at a shopping
facility, in accordance with some embodiments. In step 2302, the
central control system receives requested product information
regarding one or more product attributes corresponding to at least
one type of product for which a customer is shopping. The product
information may be specified attributes, may be product names,
generic name for a product, and/or other such information from
which product attributes can be identified. For example, the
attributes database may further associate product names with
specific attributes, and associate other product information with
types of products from which attributes can be identified, types of
attributes, and/or other such identification of attributes.
In step 2304, a set of products is identified from the product
attribute database with each product corresponding to one or more
of the received product attributes. In some embodiments, each
product has one or more product attributes of the received product
attributes. In step 2306, an assortment of one or more different
products is selected from the set of products. Often the set of
products may be more products than a customer would want to view.
As such, the assortment may have less than all of the set of
products. In other instances, however, each product of the set of
products may be selected as part of the assortment.
In step 2308, at least one of each of different products of the
assortment of products are caused to be physically collected and
positioned on a product support system at the shopping facility.
For example, instructions may be sent to a worker to retrieve one
or more products from one or more locations, the product routing
system may activate an automated system to retrieve and convey one
or more products, other such methods of retrieval and transport may
be used, or a combination of two or more of such methods. In step
2310, the central control system causes the loaded product support
system and at least the assortment of the different products to be
physically presented to the customer while at the shopping
facility.
In selecting the assortment of different products some embodiments
access a partiality vector database that associates different
customer identifiers each specific to a different customer and a
set of customer partiality vectors corresponding to a respective
one of the customer identifiers, and select the assortment of
different products that each have at least a threshold relationship
between corresponding product partiality vectors and one or more of
the customer's partiality vectors. Additionally or alternatively,
some embodiments access, from a plurality of customer profiles each
associated with a different customer and maintaining at least
purchase history information, a customer profile associated with
the customer, and selects the assortment of different products
based on at least the purchase history information of the customer
profile.
In some embodiments, the system receives, from a customer and prior
to the customer entering the shopping facility, a listing of
multiple different types of products that the customer is
interested in purchasing. A separate assortment of different
products is selected for each of the multiple different types of
products. The central control system can cause each of the
different assortments of the different products to be physically
collected prior to the customer entering the shopping facility.
Further, it can be identified when the customer is in the shopping
facility, and the system can cause the different assortments of the
different products to be physically presented to the customer.
Further, the presentation of products is not limited to a single
assortment. Some embodiments cause multiple different assortments
of different products to be simultaneously presented to the
customer. In some embodiments the products are organized according
to the different assortments. Similarly, some embodiments cause one
or more other assortments of different products to be physically
collected. The other assortment can be physically presented to
another customer, while a first assortment is physically presented
to a first customer, at a physical location within the shopping
facility that is different than a physical location where the first
assortment is being presented to the first customer.
Some embodiments detect the selection of a product of the
assortment of the different products from the product support
system by the customer. A point of sale system can receive an
identification of the product and obtains payment from the customer
for at least that product. User interface systems may be provided
to allow customer to enter attributes, submit requests, submit
queries, make payments, and/or other such actions. In some
embodiments, requested product information is received from a
customer interface system of a plurality of customer interface
systems distributed throughout the shopping facility that are each
configured to enable customers to enter product attributes of
products the customer is interested in purchasing and based on
which assortments are selected. In collecting products, some
embodiments issue an instruction to a conveyor system with a
product identifier and a storage location of each of the different
products of the first assortment and instructing the conveyor
system to retrieve each of the different products of the first
assortment.
In some embodiments, systems, apparatus and a corresponding method
performed by the systems, comprises: a plurality of product support
systems at a retail shopping facility in which customers enter to
purchase different retail products, wherein each of the product
support systems is configured to receive and support multiple
products; a central control circuit associated with the retail
shopping facility; and a product attribute database associating
products available for purchase through the retail shopping
facility with one or more product attributes that each define an
attribute of the product; wherein the central control circuit is
configured to: receive requested product information regarding one
or more product attributes corresponding to a first type of product
for which a first customer is shopping, identify from the product
attribute database a set of products with each product having
product attributes that correspond to at least one of the received
product attributes, select from the set of products a first
assortment of different products, cause at least one of each of the
different products of the first assortment to be physically
collected and positioned on at least a first product support
system, and cause the first product support system and at least the
first assortment of the different products to be physically
presented to the first customer while at the shopping facility.
Some embodiments provide methods of presenting retail products to
customers at a shopping facility, comprising: receiving, through a
central control circuit of a retail shopping facility, requested
product information regarding one or more product attributes
corresponding to a first type of product for which a first customer
is shopping; identifying a set of products that each have product
attributes that correspond to at least one of the received product
attributes; selecting from the set of products a first assortment
of different products; causing at least one of each of the
different products of the first assortment to be physically
collected and positioned on at least a first product support system
at the shopping facility; and causing the first product support
system and at least the first assortment of the different products
to be physically presented to the first customer while at the
shopping facility.
Pursuant to various embodiments, systems, apparatuses and methods
are provided herein useful to provide a customer with additional
purchase options beyond items physically adjacent to the customer.
More specifically, the embodiments described herein allow a store
to provide purchase options for items based on a customer's
location within the store within a virtual catalog. This
advantageously provides the functionality of a virtual store to a
customer at a brick-and-mortar location.
The various embodiments described herein can present purchase
options on a mobile communication device, such as a mobile
telephone, tablet, laptop, or the like, based on a customer's
location within the store. To achieve this, the location of the
customer within the store can be determined using one of several
methods and then modular and product location information for the
store can be referenced to determine items adjacent to the
customer. Based on selection of one of the items or narrowing of
the options, the embodiments described herein can focus on one item
adjacent to the customer and provide purchase options for items
within a hierarchy of that item. The hierarchy can take any desired
form, such as products commonly purchased with the item, other
options for the type of item, other sizes for the item, other
choices within the same product category, and so forth.
As illustrated in FIG. 24, a retail location 2412 can typically
include a plurality of aisles 2414 having products 2416 disposed
therealong on various displays 2418, such as shelving units,
coolers, and the like, and on feature locations 2420, which can be
located at the end of the aisles 2414, in free-standing displays,
or the like. The displays 2418 and feature locations 2420 include
product support members 2422, such as shelves and the like,
configured to receive the products 2416 thereon for display. A cart
corral 2424 is typically located near an entrance to the retail
location 2412 with carts 2426 generally contained therein. As a
customer enters the retail location 2412, the customer can
therefore get one of the carts 2426 for the shopping trip.
Thereafter, the customer will travel through the retail location
2412 collecting products 2416 and proceed to one or more
point-of-sale locations 2428 having point-of-sale devices 2430.
Details of the interacting components and structure of the
embodiments described herein are shown in FIG. 26. As illustrated,
a mobile communication device 2432 is configured to communicate
with a computing device 2434 of the retail location 2412, such as a
server or database device 2434, through one or more communication
networks 2436. The computing device 2434 can be local within the
retail location 2412 and/or can be remote therefrom, such as a
centralized system. Suitable communication networks 2436 can
include, without limitation, the Internet, a cellular network,
Bluetooth, or other communication medium, or a combination thereof.
The mobile communication device 2432 can be any suitable
communication device, such as a mobile phone, tablet, laptop,
E-reader, or the like.
Software operating on the mobile communication device 2432 can
provide the various functionalities and operations described
herein. By one approach, the software can be in the form of an
application running on the mobile communication device 2432. The
application can be available for purchase and/or download from any
website, online store, or vendor over any suitable communication
network 2436. Alternatively, a user can download the application
onto a personal computer and transfer the application to the mobile
communication device 2432. In this instance, the user downloads and
installs the app on the mobile communication device 2432. When
operation is desired, the user runs the application on the mobile
communication device 2432 by a suitable selection.
Moreover, many mobile communication devices can be locked when not
currently in use, and, in some instances, can require the entry of
a passcode or a biometric entry, such as a fingerprint scan, to
unlock the mobile communication device. In such a case, the
application can bypass the lock screen to perform one or more of
the various functions described herein in response to determining
that the user is in the retail location 2412 or can present the
various display options in front of the lock screen. Additionally,
if desired, selecting the display can direct the user to a passcode
and/or biometric entry screen, and the application can be
configured to display after correct entry of the passcode or
biometric entry
As shown in FIG. 25, the mobile communication device 2432 can
include a user input 2438, such as a touch screen, keypad, switch
device, voice command software, or the like, a receiver 2440, a
transmitter 2442, which can both be incorporated within a
transceiver, a memory 2444, a power source 2446, which can be
replaceable or rechargeable as desired, a display 2448, a location
determination device 2450, a camera device 2452 and a control
circuit 2454 controlling the operation thereof. As commonly
understood, the components are connected by electrical pathways,
such as wires, traces, circuit boards, and the like.
The term control circuit refers broadly to any microcontroller,
computer, or processor-based device with processor, memory, and
programmable input/output peripherals, which is generally designed
to govern the operation of other components and devices. It is
further understood to include common accompanying accessory
devices. These architectural options are well known and understood
in the art and require no further description here. The control
circuit 2454 may be configured (for example, by using corresponding
programming stored in a memory as will be well understood by those
skilled in the art) to carry out one or more of the steps, actions,
and/or functions described herein.
The control circuit 2454, via the location determination device
2450, can be configured to determine a location of a user. As such,
by one approach, the control circuit 2454 can monitor the location
of the mobile device 2432 and perform the functions described
herein in response to determining that the location corresponds to
a location of the retail location 2412. By another approach, the
control circuit 2454 can be configured to receive a signal from the
store computing device 2434 to identify when the customer enters
the retail location 2412. Further, the control circuit 2454 can
operate the location determination device 2450 to determine where
the customer is located within the retail location 2412.
Next, the control circuit 2454, via the transceiver 2440, 2442, can
be configured to retrieve or receive modular data and product
location data for the retail location 2412 from the server 2434.
The modular data can include the layout, as well as, the types and
configurations of product displays 2418 within the retail location
2412. The product location data can include identifications of the
products 2416 within the retail location 2412 and the locations of
the products 2416 on the various displays 2418, which can include
details such as which shelf 2422 the products are located on or the
portion of the shelf 2422 where the products 2416 are intended to
be stocked.
So configured, when the user enters the retail location 2412, the
control circuit 2454 can track the location of the user. The
location determination device 2450 can be any suitable device. By
one approach, the location determination device 2450 can use micro
or geo-locationing. By another approach, the location determination
device 2450 can utilize beacon signals received from the product
displays 2418 within the retail location 2412. By another approach,
the location determination device 2450 can utilize a non-visible
modulation of lights within the retail location 2412. By another
approach, the control circuit 2454 can receive location information
from the store computing device 2434, which can utilize camera
devices or other locationing devices within the retail location
2412.
Subsequently, or simultaneously, the control circuit 2454 can
access the modular data to determine which product display 2418 is
adjacent to the current location of the user and access the product
location data to determine which products 2416 are intended to be
stocked in that product display 2418. Thereafter, the control
circuit 2454 can determine a particular product 2456 of the
products 2416 intended to be stocked on the product display 2418
and present products 2416 in a virtual catalog within a hierarchy
of the particular product 2456 on the display 2448 of the mobile
communication device 2432. The virtual catalog can be used, for
example, to offer products for sale that are stocked at larger
format stores or available online. Advantageously, by using the
modular and product location data, the functionalities described
herein can be utilized even in a given product 2416 is out of
stock. The computing device 2434, which can be local or a remote
central system as described above, can be a host and provide
hierarchies for each product 2416 within the retail location
2412.
By one approach, the control circuit 2454 can narrow the product
options based on movement of the mobile communication device 2432.
For example, categories of products can scroll through the phone,
such as by largest to smallest, within the immediate area. By
another approach, the user can operate the camera device 2452, as
described in more detail below, to capture media of the particular
product 2456. By another approach, the control circuit 2454 can be
configured to read a machine readable code, such as a UPC, QR code,
or the like, captured by the camera device 2452.
The hierarchy of the particular product 2456 can include products
2416 commonly purchased with the particular product 2456, other
available sizes for the particular product 2456, other product
options or brands for the type of the particular product, products
2416 associated with the particular product 2456, and so forth. For
example, if the particular product 2456 is a ketchup bottle of a
first brand, the hierarchy presented can include one or more of:
other ketchup brands, other sizes for the first brand of ketchup,
associated condiments, including mustard, relish, and the like. The
control circuit 2454 can further be configured to broaden the range
of the products 2416 presented within the hierarchy, for example,
the products 2416 presented on the display can start with other
sizes, proceed to other brands, proceed to other condiments, and
proceed to broader products, such as ground beef, buns, table
clothes, grills, picnic furniture, etc. The products 2416 within
the hierarchy can be sequentially broadened based on a
predetermined amount of time elapsing, based on an input to the
user input 2438, or both.
If desired, a user can input a filter for the hierarchy that, if
applicable, will filter products 2416 within the hierarchy for the
particular product 2456 for presentation on the display 2448 of the
mobile communication device 2432. For example, the filter can be a
shopping list identified by the user, value information indicating
at least one partiality possessed by the user, described in more
detail below, products that the user doesn't like, and so
forth.
As set forth above, the mobile communication device 2432 includes
the camera device 2452. The control circuit 2454 can analyze media,
including video and/or images, captured by the camera device 2452.
The camera device 2452 can be operated by user or can be
automatically operated while the user is in the retail location
2412, viewing the virtual catalog, or the like. So configured, the
control circuit 2452 can analyze the media captured by the camera
device 2452 to identify products 2416 on the product display 2418.
As such, this functionality can be used to narrow the products 2416
to identify the particular product 2456. Moreover, if desired, the
control circuit 2452 can analyze stock levels for the various
products 2416 identified and can be configured to generate a signal
indicating that a product 2416 has a low stock in response to
determining that the product support structure 2422 has a stock
level below a predetermined amount, such as 3/4, half, 1/4, or the
like, or that the number of products 2416 is below a predetermined
number.
Further, the user can select one of the products 2416 in the
hierarchy for purchase via the user input 2438. If the product 2416
is stocked in the retail location 2412, the purchase selection can
send a signal to the computing device 2434 to generate a task for
an associate to bring the product 2416 to one of the point-of-sale
locations 28, to a customer service location, or the like. The
selection of the one or more products 2416 can further be added to
a virtual cart compiled by the control circuit 2454 and sent to the
computing device 2434. The computing device 2434 can then forward
the virtual cart to the point-of-sale devices 2430 so that a
customer can purchase both the virtual products 2416 and any
products 2416 selected in the retail location 2412 at the same
time. Alternatively, the customer can purchase the virtual items
using the mobile communication device 2432 and the user input 2438
thereof. By either approach, the control circuit 2454 can present
an option to the customer to receive the products 2416 by delivery
or pick-up.
In some embodiments, a virtual catalog apparatus is described that
includes a memory having modular and product location data for a
retail location stored thereon; a location determining device; a
display; and a control circuit coupled to the location determining
device and the display. The control circuit is configured to:
determine a location of a user with the location determining
device; access modular data of the retail location to determine a
product display adjacent to the user; access product location data
of the retail location to determine which products are intended to
be stocked on the product display; determine a particular product
of the products intended to be stocked on the product display; and
present products within a hierarchy of the particular product on
the display.
By some approaches, the hierarchy of the particular product can
include products commonly purchased with the particular product. By
further approaches, the hierarchy of the particular product can
include other available sizes for the particular product.
By several approaches, the control circuit is configured to present
products within increasingly broader aspects of the hierarchy based
on time elapsed.
By some approaches, the control circuit is configured to apply a
filter to the products within the hierarchy to be presented on the
display. By further approaches, the filter can include a shopping
list identified by the user. By further approaches, the filter can
include value information indicating at least one partiality
possessed by the user.
By several approaches, the apparatus can further include a camera
device; and wherein the control circuit can be configured to
analyze video captured by the camera device to identify products on
the product display.
By some approaches, the control circuit can further be configured
to analyze stock levels on the product display in the video
captured by the camera device and send a signal in response to
determining that a product has a low stock.
By several approaches, the control circuit can be configured to
receive a selection of one or more of the products within the
hierarchy of the particular product for purchase. By further
approaches, the control circuit can be configured to add the
selected products to a virtual cart for purchase at a point of sale
of the retail location along with any physical products. By further
approaches, the control circuit can be configured to receive a
selection of delivery or pick-up of the selected products.
In several embodiments and as shown in FIG. 27, a method 2700 of
providing a virtual catalog with a mobile device is described
herein that includes determining 2702 a location of a user with a
location determining device of the mobile device; accessing 2704
modular data stored in a memory with a control circuit of the
mobile device to determine a product display adjacent to the user;
accessing 2706 product location data stored in the memory with the
control circuit to determine which products are intended to be
stocked on the product display; determining 2708 a particular
product of the products intended to be stocked on the product
display within the control circuit; and presenting 2710 products
within a hierarchy of the particular product on a display of the
mobile device.
By some approaches, presenting the products within the hierarchy of
the particular product can include presenting products commonly
purchased with the particular product; and/or presenting products
within increasingly broader aspects of the hierarchy based on time
elapsed;
By several approaches, presenting products within the hierarchy of
the particular product can further include applying a filter to the
products within the hierarchy to be presented on the display. By
further approaches, applying the filter can include presenting
products identified in a shopping list. By further approaches,
applying the filter can include filtering products within the
hierarchy based on value information indicating at least one
partiality possessed by the user.
By some approaches, the method 2700 can further include analyzing
2712 video captured by a camera of the mobile device to identify
products on the product display with the control circuit.
By several approaches, the method 2700 can further include
analyzing stock levels on the product display in the video captured
by the camera device with the control circuit; and sending a signal
in response to determining that a product has a low stock.
By some approaches, the method 2700 can further include receiving
2714 a selection of one or more of the products within the
hierarchy of the particular product for purchase. By further
approaches, the method can include adding the selected products to
a virtual cart with the control circuit for purchase at a point of
sale of the retail location along with any physical products.
Further, the circuits, circuitry, systems, devices, processes,
methods, techniques, functionality, services, servers, sources and
the like described herein may be utilized, implemented and/or run
on many different types of devices and/or systems. FIG. 28
illustrates an exemplary system 2800 that may be used for
implementing any of the components, circuits, circuitry, systems,
functionality, apparatuses, processes, devices, or parts of such
circuits, circuitry, functionality, systems, apparatuses,
processes, or devices mentioned herein. However, the use of the
system 2800 or any portion thereof is certainly not required.
By way of example, the system 2800 may comprise a control circuit
or processor module 2812, memory 2814, and one or more
communication links, paths, buses or the like 2818. Some
embodiments may include one or more user interfaces 2816, and/or
one or more internal and/or external power sources or supplies
2840. The control circuit 2812 can be implemented through one or
more processors, microprocessors, central processing unit, logic,
local digital storage, firmware, software, and/or other control
hardware and/or software, and may be used to execute or assist in
executing the steps of the processes, methods, functionality and
techniques described herein, and control various communications,
decisions, programs, content, listings, services, interfaces,
logging, reporting, etc. Further, in some embodiments, the control
circuit 2812 can be part of control circuitry and/or a control
system 2810, which may be implemented through one or more
processors with access to one or more memory 2814 that can store
instructions, code and the like that is implemented by the control
circuit and/or processors to implement intended functionality. In
some applications, the control circuit and/or memory may be
distributed over a communications network (e.g., LAN, WAN,
Internet) providing distributed and/or redundant processing and
functionality. Again, the system 2800 may be used to implement one
or more of the above or below, or parts of, components, circuits,
systems, processes and the like.
The user interface 2816 can allow a user to interact with the
system 2800 and receive information through the system. In some
instances, the user interface 2816 includes a display 2822 and/or
one or more user inputs 2824, such as buttons, touch screen, track
ball, keyboard, mouse, etc., which can be part of or wired or
wirelessly coupled with the system 2800. Typically, the system 2800
further includes one or more communication interfaces, ports,
transceivers 2820 and the like allowing the system 2800 to
communicate over a communication bus, a distributed computer and/or
communication network (e.g., a local area network (LAN), the
Internet, wide area network (WAN), etc.), communication link 2818,
other networks or communication channels with other devices and/or
other such communications or combination of two or more of such
communication methods. Further the transceiver 2820 can be
configured for wired, wireless, optical, fiber optical cable,
satellite, or other such communication configurations or
combinations of two or more of such communications. Some
embodiments include one or more input/output (I/O) ports 2834 that
allow one or more devices to couple with the system 2800. The I/O
ports can be substantially any relevant port or combinations of
ports, such as but not limited to USB, Ethernet, or other such
ports. The I/O interface 2834 can be configured to allow wired
and/or wireless communication coupling to external components. For
example, the I/O interface can provide wired communication and/or
wireless communication (e.g., Wi-Fi, Bluetooth, cellular, RF,
and/or other such wireless communication), and in some instances
may include any known wired and/or wireless interfacing device,
circuit and/or connecting device, such as but not limited to one or
more transmitters, receivers, transceivers, or combination of two
or more of such devices.
In some embodiments, the system may include one or more sensors
2826 and/or couple with the sensor systems (e.g., sensory systems
2216) to provide information to the system and/or sensor
information that is communicated to another component, such as the
central control system, product routing system, etc. The sensors
can include substantially any relevant sensor, such as optical
based scanning sensors to sense and read optical patterns (e.g.,
bar codes), radio frequency identification (RFID) tag reader
sensors capable of reading RFID tags in proximity to the sensor,
distance measurement sensors (e.g., optical units, sound/ultrasound
units, etc.), cameras and image processing systems, and other such
sensors. The foregoing examples are intended to be illustrative and
are not intended to convey an exhaustive listing of all possible
sensors. Instead, it will be understood that these teachings will
accommodate sensing any of a wide variety of circumstances in a
given application setting.
The system 2800 comprises an example of a control and/or
processor-based system with the control circuit 2812. Again, the
control circuit 2812 can be implemented through one or more
processors, controllers, central processing units, logic, software
and the like. Further, in some implementations the control circuit
2812 may provide multiprocessor functionality.
The memory 2814, which can be accessed by the control circuit 2812,
typically includes one or more processor readable and/or computer
readable media accessed by at least the control circuit 2812, and
can include volatile and/or nonvolatile media, such as RAM, ROM,
EEPROM, flash memory and/or other memory technology. Further, the
memory 2814 is shown as internal to the control system 2810;
however, the memory 2814 can be internal, external or a combination
of internal and external memory. Similarly, some or all of the
memory 2814 can be internal, external or a combination of internal
and external memory of the control circuit 2812. The external
memory can be substantially any relevant memory such as, but not
limited to, solid-state storage devices or drives, hard drive, one
or more of universal serial bus (USB) stick or drive, flash memory
secure digital (SD) card, other memory cards, and other such memory
or combinations of two or more of such memory, and some or all of
the memory may be distributed at multiple locations over the
computer network. The memory 2814 can store code, software,
executables, scripts, data, content, lists, programming, programs,
log or history data, user information, customer information,
product information, and the like. While FIG. 28 illustrates the
various components being coupled together via a bus, it is
understood that the various components may actually be coupled to
the control circuit and/or one or more other components
directly.
Some embodiments provide systems comprising: a database of user
profiles, the user profiles having one or more partialities
associated therewith; a database of retail products, at least some
of the retail products having identified vectorized product
characterizations; a control circuit configured to access the
database of user profiles and the database of retail products and
configured to identify one or more suggested retail items for a
particular customer based, in part, on comparisons between the
identified partialities of the user profile associated with the
particular customer and the identified vectorized product
characterizations of the retail products; and a plurality of
physical shopping carts at a retail shopping facility, one of the
plurality of shopping carts being designated for the particular
customer and filled with at least one of the one or more suggested
retail items prior to the particular customer arriving at the
retail shopping facility. In some implementations, the system
further comprising a staging area within the retail shopping
facility with numerous shopping carts designated for use by
particular customers, the designated shopping carts filled with at
least one of the one or more suggested retail items for the
particular customers, the staging area being where the particular
customers retrieve their designated shopping carts.
The retail shopping facility may further include a rejection bin
where the particular customer may deposit unwanted suggested retail
items. In some embodiments, the control circuit is further
configured to update the one or more partialities in the database
of user profiles based, in part, on the particular customer
associated with the user profile placing one of the unwanted
suggested retail items in the rejection bin and the rejection bin
having a bin sensor configured to detect placement of the unwanted
suggested retail items. A user profile, in some instances, has
purchased retail products associated therewith and at least one
identified partiality associated with the purchased retail
products.
In some embodiments, the system further comprising one or more
point of sale terminals and the control circuit is further
configured to update the user profile associated with the
particular customer based, in part, on retail products purchased by
the particular customer at the point of sale terminal and the
identified vectorized product characterizations associated with the
purchased retail products. The control circuit, in some
applications, is configured to receive, from the particular
customer, a collection time and a collection location for picking
up the particular customer's designated shopping cart filled with
the at least one of the one or more suggested retail items. The
control circuit may further receive location information from the
particular customer and the at least one of the one or more
suggested retail items are loaded into the designated shopping cart
as the particular customer approaches the retail shopping facility.
In some implementations, the database of user profiles further
includes a purchase history and the control circuit is further
configured to identify the one or more suggested retail items
based, in part, on the purchase history of the particular
customer.
The control circuit, in some embodiments, is configured to update
the database of user profiles according to purchases at multiple
retail facilities. In some implementations, the partialities are
represented by partiality vectors and can include values,
preferences, and affinities. The control circuit may further be
configured to analyze the partiality vectors and the vectorized
product characterizations and identify an overlap therebetween.
Some embodiments provide methods comprising: maintaining a database
of a plurality of user profiles having and one or more identified
partialities associated therewith; maintaining a database of retail
products, at least some of the retail products having identified
vectorized product characterizations; identifying one or more
suggested retail items for a particular customer based, in part, on
comparisons between the identified partialities of the user profile
associated with the particular customer and the identified
vectorized product characterizations of the retail products; and
loading a designated shopping cart, at a retail shopping facility,
for the particular customer with at least one of the one or more
suggested retail items prior to the particular customer arriving at
the retail shopping facility. In some implementations, the method
further comprising providing the designated shopping cart at a
staging area within the retail shopping facility. The method may
further comprise updating the user profile based, in part, on the
particular customer rejecting one of the suggested retail items
loaded into the designated shopping cart.
In some embodiments, the method further comprises updating the user
profile based, in part, on information received about the
particular customer from a point of sale terminal visited by the
particular customer before exiting the retail shopping facility.
Some applications further comprise receiving, from the particular
customer, a collection time and a collection location for picking
up the particular customer's designated shopping cart filled with
the one or more suggested retail items. In some implementations,
the method further comprises receiving location information from
the particular customer and loading the one or more suggested
retail items into the designated shopping cart as the particular
customer approaches the retail shopping facility.
Some embodiments provide systems to enhance customers' retail
shopping experiences, comprising: a retail environment control
circuit coupled with memory storing instructions that when executed
by the control circuit cause the control circuit to: identify that
a first customer is present at a retail shopping facility; access a
customer profile database, wherein the customer profile database
maintains a customer profile for each of multiple different
customers, and each customer profile comprises a set of customer
partiality vectors corresponding to the customer, wherein the
customer partiality vectors are directed quantities that each have
both a magnitude and a direction, with the direction representing a
determined order imposed upon material space-time by a particular
partiality and the magnitude represents a determined magnitude of a
strength of the belief, by the corresponding customer, in a benefit
that comes from that imposed order; identify a first set of
recommended products each having at least a threshold relationship
between corresponding product partiality vectors and one or more of
a set of partiality vectors associated with the first customer; and
communicate a recommendation listing of the first set of
recommended products and causing at least a portion of the
recommendation listing to be presented to the first customer while
the first customer is still physically at the shopping facility. In
some implementations the control circuit is configured to receive a
response from the customer indicating a level of agreement of the
recommendation corresponding to at least one recommended product of
the first set of recommended products. Further, the control circuit
can be configured to adjust at least one partiality vector of the
first set of partiality vectors based on the level of
agreement.
In some applications, the control circuit in communicating the
recommendation listing wirelessly communicates the recommendation
listing of the first set of recommended products to a mobile user
interface unit associated with the first customer and causes
content representative of the recommendation listing to be
displayed on the user interface unit. In some embodiments, the
system further comprises a series of multiple display systems each
positioned at different locations throughout the shopping facility,
wherein the control circuit is further configured to obtain a
location of the first customer within the shopping facility. The
control circuit in communicating the recommendation listing can, in
some instances, communicate the recommendation listing to a first
display system of the multiple display systems that is within a
threshold distance of the obtained location of the first
customer.
In some embodiments, the system further comprises a series of
multiple audio output systems each positioned at different
locations throughout the shopping facility, wherein the control
circuit is further configured to obtain a location of the first
customer within the shopping facility; and wherein the control
circuit in communicating the recommendation listing communicates
the recommendation listing to a first audio output system of the
multiple audio output systems that is within a threshold distance
of the obtained location of the first customer. In some instances,
the control circuit is configured to identify, from product
information of a first recommended product of the first set of
recommended products, at least a first product partiality vector
that has the threshold relationship with at least one of the first
customer's partiality vectors; and cause marketing information,
representative of at least the first product partiality vector
associated with the first recommended product, to be displayed as
part of the recommendation listing being presented to the first
customer. The control circuit, in some implementations, is
configured to: obtain a location of the first customer within the
shopping facility, wherein control circuit in identifying the first
set of recommended products identifies each of the recommended
products of the first set of recommended products based on the
first set of partiality vectors and on the location of the first
customer within the shopping facility. In some embodiments, the
control circuit in identifying the first set of recommended
products identifies each of the recommended products of the first
set of recommended products that each correspond to at least one
product previously selected by the first customer during the first
customer's current visit to the shopping facility.
Some embodiments provide methods of enhancing customers' retail
shopping experiences, comprising: by a retail environment control
circuit: identifying that a first customer is present at a retail
shopping facility; accessing a customer profile database, wherein
the customer profile database maintains a customer profile for each
of multiple different customers, and each customer profile
comprises a set of customer partiality vectors corresponding to the
customer, wherein the customer partiality vectors are directed
quantities that each have both a magnitude and a direction, with
the direction representing a determined order imposed upon material
space-time by a particular partiality and the magnitude represents
a determined magnitude of a strength of the belief, by the
corresponding customer, in a benefit that comes from that imposed
order; identifying a first set of recommended products each having
at least a threshold relationship between corresponding product
partiality vectors and one or more of a set of partiality vectors
associated with the first customer; and communicating a
recommendation listing of the first set of recommended products and
causing the recommendation listing to be presented to the first
customer while the first customer is still in the shopping
facility. In some applications, the method further comprises
receiving a response from the customer indicating a level of
agreement of the recommendation corresponding to at least one
recommended product of the first set of recommended products. The
method may further comprise adjusting at least one partiality
vector of the first set of partiality vectors based on the level of
agreement. The communicating of the recommendation listing, in some
implementations, comprises wirelessly communicating the
recommendation listing of the first set of recommended products to
a mobile user interface unit associated with the first customer and
causing content representative of the recommendation listing to be
displayed on the user interface unit.
In some embodiments, the method further comprises: obtaining a
location of the first customer within the shopping facility; and
wherein the communicating the recommendation listing comprises
communicating the recommendation listing to a first display system,
of a series of multiple display systems each positioned at
different locations throughout the shopping facility, that is
within a threshold distance of the location of the first customer.
In some embodiments, the method further comprises: obtaining a
location of the first customer within the shopping facility;
wherein the communicating the recommendation listing comprises
communicating the recommendation listing to a first audio output
system, of a series of multiple audio output systems each
positioned at different locations throughout the shopping facility,
that is within a threshold distance of the location of the first
customer. The method may further comprise: identifying, from
product information of a first recommended product of the first set
of recommended products, at least a first product partiality vector
that has the threshold relationship with at least one of the first
customer's partiality vectors; and causing marketing information,
representative of at least the first product partiality vector
associated with the first recommended product, to be displayed as
part of the recommendation listing being presented to the first
customer. In some applications, the method further comprises:
obtaining a location of the first customer within the shopping
facility; wherein the identifying the first set of recommended
products comprises identifying each of the recommended products of
the first set of recommended products based on the first set of
partiality vectors and on the location of the first customer within
the shopping facility. The identifying of the first set of
recommended products can comprise identifying each of the
recommended products of the first set of recommended products that
each correspond to at least one product previously selected by the
first customer during the first customer's current visit to the
shopping facility.
Some embodiments provide retail product presentation systems,
comprising: a plurality of product support systems at a retail
shopping facility in which customers enter to purchase differ
References